Deep Learning in Cardiovascular Tissue Engineering: A Review on Current Advances and Future Perspectives

Abstract

The development of cardiovascular tissue engineering is a promising area of study in regenerative medicine, offering innovative solutions for restoring damaged cardiac structures. However, traditional methods face multiple limitations, including the complexity of scaffolds, optimal recellularization, and functional tissue maturation. At the same time, deep learning has demonstrated significant potential in biomedicine and is increasingly being explored to optimize processes. This review examines recent benefits in cardiovascular tissue engineering and the applicability of deep learning in this domain, highlighting the benefits of artificial intelligence (AI) algorithms in scaffold modeling, cellular interaction analysis, and tissue regeneration prediction. Additionally, we discuss major challenges in integrating AI, such as the lack of large, standardized datasets; the need for interpretable models for clinical use; and ethical and regulatory constraints. Despite these limitations, recent progress in AI and the availability of advanced machine learning techniques provide promising perspectives for transforming regenerative medicine. Future research should focus on improving access to relevant data, developing explainable AI models, and integrating these technologies into personalized medicine, ultimately accelerating the progression of cardiovascular tissue engineering from an experimental stage to clinical utilization.

1. Introduction

This review is structured into two main sections: cardiovascular tissue engineering, covering fundamental approaches and technical challenges, and deep learning applications in regenerative medicine, exploring AI-driven methods for scaffold optimization and personalized treatments.

1.1. The Importance of Cardiac Regeneration: Context and Global Impact of Cardiovascular Diseases

The ability to grow heart tissue is a new, innovative concept that could change how we approach treating cardiovascular diseases. Despite the difficulties faced, modern developments in biomaterials, gene therapies, and cellular reprogramming offer hope for advancing the field of regenerative cardiac medicine.

Tissue engineering as a scientific discipline began to take form in the mid-1980s through the pioneering work of Joseph Vacanti and Robert Langer, whose well-known “human ear on a mouse” experiment symbolized the field’s early potential. In their landmark study, Vacanti, Langer, and colleagues demonstrated that synthetic polymer scaffolds seeded with chondrocytes could serve as a template for new cartilage formation [1]. Around the same time, early investigations into cardiovascular tissue engineering, including heart valve scaffolds, also began to emerge based on their techniques. These foundational studies laid the conceptual and technical groundwork for today’s applications in cardiac regeneration.

Figure 1 summarizes the introduction and shows how the studies are organized by important categories. The topics on cardiac regeneration, limitations of transplantation, and tissue engineering technology development are relatively well covered, underscoring their importance to regenerative medicine. In addition, AI in medicine has become immensely popular lately, which suggests that this is an area of medicine that is highly active.

This review focuses on the newest developments in cardiovascular tissue engineering and the role that deep learning (DL) has in this industry. It is divided into two primary sections:

• Cardiovascular tissue engineering, which looks at fundamental approaches to cardiac tissue engineering, including scaffold design and optimization, recellularization, and the application of bioreactors for tissue engineering. Unresolved issues also include examining the integration of a scaffold into native tissue, vascularization of the scaffold and biocompatibility, and the scaffold’s mechanical stability.
• Deep learning in tissue engineering which seeks to understand how information technologies are working with AI-enhanced regenerative medicine. This review discusses DL solutions for scaffold design and modeling, analysis of cell interactions, custom-tailored treatments, and optimization of regenerative medicine, which these technologies would most certainly advance in a more effective clinical outcome.

Despite the great benefits that these AI-powered techniques can bring to tissue engineering, their application in the biomedical field comes with challenges. This review works on more stringent issues like the lack of properly formed and documented datasets, AI model explainability, and other ethical and regulatory issues that need to be solved for practical use. This article distinguishes itself from past reviews by considering the issue of traditional versus AI-based approaches in cardiovascular tissue from a comparative perspective.

To illustrate current research trends in cardiovascular tissue engineering, Figure 2 presents the distribution of the discussed studies according to their year of publication (2015–2024). This distribution highlights the ongoing advancements in the field and the increasing recognition of regenerative therapies as a promising solution for cardiovascular diseases.

According to the World Health Organization, cardiovascular diseases CVDs account for approximately 17.9 million deaths every year. Heart disease is still the number one killer globally. It is concerning to know that this number may exceed 23.6 million by the end of 2030 [2]. This highlights the burden of CVD in the world. It acts as an indicator for new treatment strategies that mitigate heart failure [3,4].

The preexisting incomplete recovery of heart muscles represents a gradual decline in heart efficiency post-surgery. Any myocardial tissue trauma, like infarction, will lead to cardiac function deterioration without prompt surgical intervention. However, current measures such as medications have proven effective in healing damaged heart tissues [5]. All these factors have made cardiac regeneration a phenomenon that has chances to drastically improve myocardial function.

Strategies such as cardiomyocyte proliferation, cell transplantation, tissue engineering, and cellular reprogramming offer new possibilities beyond conventional treatments [6]. CVDs encompass a broad spectrum of conditions, including myocardial infarction, heart failure, and arrhythmias, significantly impacting public health and the global economy. The costs associated with CVD treatment and patient care are substantial and continue to rise [3].

These regenerative strategies hold promise, particularly in conditions where native tissue cannot repair itself. However, not all cardiovascular conditions are suitable for regenerative medicine. Regenerative cardiac therapies are primarily indicated in diseases characterized by irreversible cardiomyocyte loss. These include the following:

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Myocardial infarction, where cardiac tissue is replaced by fibrotic scar, limiting contractile function;

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Ischemic cardiomyopathy, which leads to chronic ventricular dysfunction due to extensive tissue damage and remodeling;

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Congenital myocardial defects, where bioengineered tissues may assist in reconstructing missing or malformed cardiac segments.

These scenarios represent cases where cardiomyocyte-based regeneration could provide functional recovery. In contrast, conditions such as valvular disorders, arrhythmias, or hypertensive hypertrophy may not benefit from such approaches and require alternative therapeutic strategies.

Although improvements in pharmacotherapy and interventional cardiology have increased patient survival, these therapies are just palliative and do not address the permanent loss of cardiomyocytes, which is the primary cause of heart failure. Heart transplantation is now the only treatment available for patients with end-stage heart failure; however, the scarcity of donor organs and the dangers of immunosuppressive post-transplant therapy highlight the need for alternative therapeutic approaches [7].

Cardiomyocytes’ very limited proliferation potential in mammals limits efficient cardiac regeneration following infarction [8]. Utilizing cardiomyocyte proliferation, an essentially nonexistent mechanism in mammals, species like zebrafish and amphibians may regenerate their hearts [9].

Although hydrogels and decellularized extracellular matrices show great promise for heart regeneration, incorporating these materials into natural tissue still presents a big difficulty [10]. One of the primary obstacles is the immune response and inflammatory reaction that can lead to biomaterial rejection [11]. Recent studies have shown that certain signaling pathways can be manipulated to promote adult cardiomyocyte proliferation. For instance, activation of the Hippo-YAP/TAZ pathway has demonstrated potential for cardiac regeneration in preclinical models [12]. Additionally, non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs, are currently being explored as therapeutic factors for promoting cardiac regeneration [13].

1.2. Current Limitations of Heart Transplantation and the Need for Alternative Approaches

Heart transplantation remains the most effective treatment for end-stage heart failure, significantly improving both survival rates and quality of life [14]. However, this therapy is limited by several challenges, including a severe shortage of donor organs, risks associated with lifelong immunosuppression, and the potential for organ rejection and transplant dysfunction over time [15].

Concerning these issues, the focus of more recent works has been on developing tissue engineering, cardiac xenotransplantation, and regenerative medicine that can substitute orthodox transplantation techniques [16]. In this document, we discuss the current problems of heart transplantation and review prospective solutions with a special focus on new technologies and their potential use in practice.

The number of individuals receiving heart transplants across the globe is still dwarfed by the available donor organs. More than 30,000 new patients are added to the transplant lists every year, and only around 6000 transplants take place in the world every year [17]. To increase organ availability, several strategies have been proposed, including:

Utilization of donation after circulatory death (DCD) donors—While this method has gained popularity, it presents challenges related to prolonged ischemia and functional recovery of the organ [18].

Expansion of marginal donor criteria—Accepting hearts from older donors or those with comorbidities requires advanced preservation technologies and functional assessment methods [15].

The host’s immune response against the transplanted organ remains a primary limitation of heart transplantation. Despite advances in immunosuppressive therapies, transplant recipients face significant risks, including the following:

• Acute and chronic rejection—Antibody-mediated rejection is a major cause of graft failure, necessitating more effective desensitization strategies [19];
• Opportunistic infections and malignancies—Immunosuppressive therapy significantly increases the risk of severe infections and cancer development [20].

Tissue engineering offers a promising alternative for myocardial regeneration, utilizing decellularized extracellular matrices (ECM), biomimetic hydrogels, and 3D bioprinting [16]. These approaches may enable the creation of bioengineered cardiac patches for infarct repair and the process of repopulating decellularized scaffolds with autologous stem cells to create bioartificial organs.

Due to the limitations of donor-dependent transplantation, researchers are actively exploring tissue engineering and xenotransplantation, which could eliminate reliance on human donors.

Cardiac xenotransplantation has become a major research focus, particularly with advances in genetic modification of porcine organs to enhance compatibility with humans. The first successful heart transplant from a pig to a human has demonstrated feasibility; however, challenges related to hyperacute rejection and physiological incompatibility remain significant barriers [20].

Another promising strategy involves cellular and genetic therapies aimed at inducing cardiac regeneration. These approaches include the following:

• Direct cellular reprogramming—Studies have demonstrated the ability to convert fibroblasts into cardiomyocytes, eliminating the need for transplantation [21];
• Advanced nanotherapies—The targeted delivery of microRNAs and exosomes has shown potential for stimulating cardiac regeneration and reducing immune rejection [22].

Although the major treatment for end-stage heart failure is still heart transplantation, the advancement of xenotransplantation, bioartificial organs, and regenerative treatments may change the face of cardiovascular medicine in the future.

1.3. Tissue Engineering’s Function and Developments in Decellularized Scaffolds

One of the primary concerns in cardiovascular medicine is recovering myocardial tissue functionality after an ischemic or degenerative injury. The objective is difficult to achieve because of the low regenerative potential of adult cardiomyocyte tissues. In such cases, solving cardiac repair problems through tissue engineering appears to be an effective approach [23].

Of these methods, decellularized extracellular matrix (ECM)-based scaffolds are considered the best biomaterials due to their high compatibility, complex and intricate three-dimensional shapes, and bioactive factors aiding in cell proliferation and differentiation [24].

Decellularized scaffolds are created by removing all cellular components from tissues or organs while preserving the structure of the extracellular matrix (ECM). This process provides an optimal environment for recellularization, enhancing tissue integration and regeneration. As a result, decellularized scaffolds are widely used in developing bioengineered myocardial patches, heart valves, and blood vessels [25].

This review examines recent advancements in decellularization techniques, recellularization strategies, and emerging clinical applications in cardiac tissue engineering.

The technique of decellularization is important for eliminating cellular antigens while ensuring that the ECM maintains its structure and biological activity [26]. These processes are as follows:

• Physical methods—Repeated freeze–thaw cycles, variable pressure perfusion, and laser irradiation. These techniques are very effective in breaking down cellular membranes, but capsules and collagen, and proteoglycans, can be affected [27].
• Chemical methods—Anionic detergents like SDS and Triton X-100, and weak acids and bases, can effectively extract cells’ structures via chemical methods. However, if one is not careful during excessive treatment, the mechanical strength of the scaffold may be compromised as well [28].
• Enzymatic methods—Using DNase and RNase processes to get rid of residual genetic material whilst keeping the ECM itself’s biological components intact [29].

The functional scaffolds of the ECM provide support for cells that are to be integrated into the native tissue. Lately, it was uncovered that scaffold, decellularized in such a manner, can be rapidly populated with both pluripotent and mesenchymal stem cells, and cardiac fibroblasts [25].

Methods for enhancing recellularization cover the following:

• Placing growth or differentiation factors in combination with the scaffolds to induce angiogenesis and cell multiplication.
• Bioreactors serve as a device for mechanical and electrical stimulation, which helps to achieve alignment and maturation of cardiomyocytes [30].

This review examines recent advances in decellularized scaffolds for cardiac regeneration, focusing on decellularization methods, recellularization strategies, and emerging clinical applications.

To facilitate scaffold integration, bioreactors have been created to enable controlled perfusion of nutrients and mechanical stimulation and maturation of recellularized cells [29]. Preclinical research suggests that the presence of dynamic bioreactors may promote alignment, contractile activity, and vascularization of cardiomyocytes, which may improve clinical applicability.

Although the preclinical results are promising, the clinical utilization of decellularized scaffolds is limited by the need to improve mechanical stability, immune response modulation, and recellularization strategies [31]. Next, we will investigate 3D bioprinting and hybrid biomaterials for personalized scaffolds used in regenerative medicine.

Decellularized ECM-based tissue engineering poses a new paradigm for cardiovascular regeneration. The latest developments in decellularization, recellularization, and bioreactor technology are rendering clinical applications more feasible.

1.4. The Potential of Deep Learning in Optimizing Regenerative Therapies

Regenerative medicine integrates tissue engineering, cell therapy, or biotechnology to restore or swap out injured organs or tissues that suffer due to a disease, trauma, or aging. Nonetheless, even with tremendous strides being made, there are several roadblocks to clinical application of regenerative medicine, including biological variability prediction and model robustness, and these render the process of recellularization and differentiation very complicated [32].

In this context, artificial intelligence (AI) and deep learning (DL) have emerged as powerful tools for enhancing regenerative therapies by analyzing complex datasets, optimizing biomaterials, personalizing treatments, and predicting clinical outcomes [33]. This review explores recent advancements in machine learning (ML) applications in regenerative medicine, concentrating on scaffold optimization, bioreactors, cell therapy, and personalized medicine.

The use of decellularized scaffolds and complex biomaterials forms the basis of tissue engineering, and ML has a key role in their optimization through:

• Predicting scaffold biochemical and mechanical behavior with neural networks that relate material composition to function [34].
• Improving 3D bioprinting with AI-based algorithms that modify construction parameters for better cell and growth factor distribution [31].
• Assessing cell distribution within scaffolds with deep learning techniques to achieve enhanced recellularization homogeneity [25].
• Emulating AI cell behavior predictions and cardiomyocyte maturation in decellularized scaffolds through cell-ECM modeling [29].
• Altering cell culture settings by automatically controlling fluidic and oxygen supply and tissue-specific growth factor concentrations [30].
• AI-driven simulation of optimal electrical and mechanical stimuli for cardiomyocyte maturation [35].
• Automated classification and isolation of live stem cells from microscopic images using convolutional neural networks [36].
• Predicting cellular differentiation rates with AI models to anticipate the efficiency of stem cell conversion into specific tissues [37].
• Optimizing gene therapy delivery by using AI to improve viral vector and nanoparticle efficiency in genome editing applications [38].
• Personalizing regenerative treatments, correlating clinical data with the effectiveness of regenerative therapies for individualized patient care [39].

The integration of deep learning in regenerative medicine presents significant opportunities for advancing scaffold engineering, bioreactor optimization, cell-based therapies, and personalized medicine. In the future, the combination of ML with synthetic biology, 3D bioprinting, and advanced nanotechnology is expected to revolutionize regenerative treatments and accelerate their transition into clinical applications.

2. Cardiovascular Tissue Engineering: Current Status

In recent decades, medical research has significantly contributed to increased life expectancy; however, modern medicine continues to face major challenges, such as chronic degenerative diseases and the shortage of transplantable organs. Despite the growing interest and advancements in tissue engineering, the lack of universal standards for assessing decellularization completeness and process suitability remains a significant concern.

To better structure the literature reviewed in this article, Figure 3 presents a thematic classification of selected references in this section, covering studies published between 2015 and 2024.

The use of tissue engineering for the regeneration of cardiac tissue has proven to be a worthwhile area of exploration, especially as it seeks to provide the best options for treating heart failure. It is necessary to utilize a decellularized extracellular matrix (dECM) with appropriate cell types, recellularization of dECM, and bioreactors for the conditioning of tissue to develop functional skeletal organs that will have an architecture and biomechanical features similar to the myocardium.

A predominance of studies focusing on recellularization methods and bioreactors is observed, highlighting the importance of these aspects in optimizing regenerative strategies. This distribution does not necessarily reflect general trends in the literature but rather the thematic structure of the references included in this review.

In the following sections, each of these key aspects will be examined in detail, emphasizing recent advancements and remaining difficulties in cardiovascular tissue engineering.

2.1. Principles of Tissue Engineering in Cardiac Regeneration

Cardiovascular diseases are the most damaging and kill the most people in the world. It has gravely affected the health status of the people and the economy of the country [39]. The irreversible damage to myocardial tissue following myocardial infarction (MI) or other ischemic conditions necessitates the development of innovative therapeutic strategies for heart regeneration.

While pharmacological treatments and mechanical assist devices, such as ventricular assist pumps, can provide temporary improvements in cardiac function, they cannot restore lost myocardial tissue. Moreover, heart transplantation remains severely limited by organ availability and the risks associated with lifelong immunosuppression [40].

One of the most prominent clinical implementations of cardiovascular tissue engineering has been led by Toshiharu Shinoka and colleagues at Nationwide Children’s Hospital (Columbus, OH, USA). In 2011, his group initiated a clinical program focused on tissue-engineered right ventricular outflow tract (RVOT) conduits for the correction of congenital heart defects. Their work demonstrated the safety and feasibility of bioengineered vascular grafts in pediatric patients, including evidence of host cell repopulation, structural integrity, and growth potential. Notable publications detail successful mid- to long-term follow-up results [41,42] confirming the translational promise of engineered conduits in a real-world clinical setting.

In this context, cardiovascular tissue engineering has emerged as an interdisciplinary field integrating advanced biomaterials, cell therapy, and emerging biotechnologies to develop functional tissue structures capable of repairing or replacing damaged myocardium [43].

This chapter reviews recent developments in cardiovascular tissue engineering, with emphasis on decellularized scaffolds, recellularization techniques, and bioreactors for functional cardiac tissue development.

2.2. Decellularized Extracellular Matrix (dECM): Properties and Applications

The decellularized extracellular matrix (dECM) is a gold-standard biomaterial in cardiovascular tissue engineering, widely used for the development of scaffolds designed to support myocardial regeneration. Through specialized decellularization processes, dECM retains its three-dimensional architecture, protein composition, and bioactive factors, promoting cell attachment and cardiomyocyte differentiation [44,45].

The benefits of dECM-based scaffolds in cardiac regeneration include:

• Preservation of the native cardiac tissue architecture, providing an optimal microenvironment for regeneration [28,46].
• Enrichment in key ECM proteins, such as collagen, laminin, fibronectin, and proteoglycans, which support cell adhesion and cardiomyocyte differentiation [47].
• Fibrillar collagen structure, ensuring uniform mechanical stress distribution, is essential for cardiomyocyte maturation [45].
• Removal of cellular antigens significantly reduces the risk of post-implantation immune rejection [48].
• Lower inflammatory response compared to synthetic biomaterials, enhancing scaffold integration into native tissue [28,49].
• Effective removal of foreign DNA and RNA, minimizing the risk of adverse immune reactions [47].
• Presence of pro-angiogenic factors (VEGF, FGF, TGF-β), which stimulate neovascularization and improve tissue vascularization [46,48].
• Supports endothelial cell migration and proliferation, facilitating post-implantation revascularization [50].
• Chemical crosslinking strategies, improving mechanical stability and increasing scaffold durability in biological environments [49].
• Incorporation of RGD peptides (arginine-glycine-aspartic acid) to enhance cellular interactions and adhesion [48].
• Bioactive growth factor integration, accelerating stem cell maturation and differentiation into cardiomyocytes [46].
• Versatility in clinical applications, with dECM scaffolds used in cardiac patches, artificial valves, and vascular grafts [28,50].
• Potential for combination with advanced technologies, such as 3D bioprinting and cell-based therapies, to enable personalized cardiac regeneration solutions [47].

To ensure the effective removal of cellular components while preserving ECM integrity, various decellularization techniques have been developed. These methods are categorized into chemical, enzymatic, and physical methods (Figure 4), each with distinct advantages and limitations

Despite its promising features, dECM-based scaffolds also present several challenges and limitations:

• Loss of structural integrity due to the decellularization process, which affects mechanical stability and resistance to myocardial stress [44].
• Weaker mechanical properties compared to native cardiac tissue, potentially leading to insufficient contractile strength for myocardial integration [23].
• Fragility of dECM scaffolds, limiting their use in bioprinting and surgical handling, and requiring crosslinking and stiffness optimization [48].
• High variability in composition, influenced by tissue source (myocardium, pericardium, blood vessels) and decellularization method [45].
• Lack of standardized ECM composition, making it difficult to compare results across studies and hindering clinical translation [23].
• Potential loss of essential ECM proteins (fibronectin, laminin) and growth factors during decellularization, reducing scaffold regenerative potential [46].
• Absence of a pre-existing vascular network, affecting oxygen diffusion and implanted cell survival [46].
• Limited integration with host tissue due to insufficient angiogenic signaling [48].
• Emerging technologies, such as pro-angiogenic growth factor injections or pre-vascularized bioprinting, are being explored to overcome these limitations [51].
• Complex and multi-step decellularization protocols, increasing processing time and production costs for clinical applications [49].
• Challenges in scaffold sterilization, as many chemical and physical methods may alter mechanical and biochemical properties [45].
• Lack of standardized production methods, limiting scalability for clinical use [46].

A detailed table follows, providing an in-depth analysis of each method used in the decellularization process, focusing on efficacy, mechanism of action, benefits, drawbacks, and study outcomes (

Table 1. Comparative analysis of decellularization methods.

Method	Mechanism of Action	Advantages	Disadvantages	References and Experimental Findings
Chemical (SDS, Triton X-100, CHAPS, Acids/Bases)	Ionic and non-ionic detergents solubilize cell membranes and remove DNA and intracellular proteins.	✔ Efficient removal of cells and antigens. ✔ Preserves ECM structure at optimal concentrations.	• High concentrations may degrade collagen and bioactive proteins. • Requires extensive washing steps to eliminate toxic residues.	A study on porcine cardiac tissue treated with 1% SDS for 12 h demonstrated complete cell removal while maintaining ECM structural integrity [45]. Using 0.5% SDS for 9 h resulted in the preservation of key ECM components [47]. A comparison of SDS and Triton X-100 showed that SDS efficiently removes cells but leads to the loss of essential structural proteins, whereas Triton X-100 preserves ECM composition more effectively [46]. A combined SDS and DNase protocol improved DNA removal by 98% [49]. Treatment with SDS followed by extensive PBS washing reduced scaffold cytotoxicity, facilitating cell repopulation [23].
Enzymatic (DNase, RNase, Trypsin, Collagenase)	Enzymes selectively degrade genetic material and cellular proteins.	✔ Preserves ECM structure and bioactivity. ✔ Reduces scaffold antigenicity.	• High concentrations may damage structural proteins. • Requires optimization for different tissue types.	A study on porcine heart valves treated with Trypsin and EDTA for 48 h resulted in incomplete cell removal and exposed collagen to immune responses [49]. A protocol using DNase and RNase after freeze–thaw lysis improved DNA clearance by 98% [47]. A comparison of collagenase and DNase in cardiac ECM decellularization found that this method better preserved ECM protein composition than SDS [46]. In human cardiac tissue, DNase and RNase treatment at controlled temperatures effectively removed DNA without compromising collagen integrity [48]. RNase-based decellularization prevented immune activation upon implantation, outperforming chemical methods [28]. A DNase/Collagenase protocol enabled efficient recellularization with mesenchymal stem cells, highlighting potential regenerative applications [50].
Physical (Temperature, Pressure, Perfusion, Sonication, Agitation, Electroporation)	Mechanical stress disrupts cell membranes, facilitating intracellular content removal.	✔ No need for toxic chemical agents. ✔ Preserves ECM biomechanical integrity.	• May result in incomplete cell removal. • Can damage collagen fiber structure.	Repeated freeze–thaw cycles at −80 °C for 16 h effectively removed cellular material but caused structural disruptions in ECM [23]. High hydrostatic pressure decellularization completely removed cells from aortic roots, but led to structural alterations [51]. Perfusion with hypotonic solutions followed by controlled sonication achieved efficient cell removal, though it weakened ECM protein networks [47]. Low-frequency ultrasonic waves (sonication) improved cell debris removal while maintaining ECM stiffness [28]. A combined electrostimulation and agitation method accelerated cell removal while preserving ECM architecture [46]. In cardiac tissue decellularization, low-voltage electrostimulation increased nuclear clearance rates but negatively impacted elastic fibers [49].

).

2.3. Recellularization Methods—Stem Cells, iPSC-Derived Cardiomyocytes, Bioactivation of Scaffolds

Cardiovascular diseases are the leading cause of mortality worldwide, with an increasing prevalence and a significant impact on healthcare systems [52]. Myocardial regeneration through cell transplantation has been extensively explored; however, limitations regarding retention, viability, and cellular integration have led to the development of advanced recellularization strategies based on biomimetic scaffolds [53].

Recellularization involves repopulating decellularized scaffolds with functional cells, using embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and mesenchymal stem cells (MSCs) [54]. This technology has the potential to restore cardiac function, offering a viable alternative to heart transplantation [55]. Due to their plasticity, stem cells are fundamental for scaffold recellularization and myocardial regeneration. The most commonly used types are embryonic stem cells (ESCs), which have a high differentiation potential but raise ethical concerns and a risk of teratoma formation [56], and induced pluripotent stem cells (iPSCs), which can be genetically customized, eliminating the risk of immune rejection but exhibiting incomplete maturation into cardiomyocytes [57].

Mesenchymal stem cells (MSCs) secrete growth factors and cytokines, exerting a pro-regenerative paracrine effect [58]. Moreover, in an in vivo study on myocardial infarction models, MSCs implanted into a decellularized scaffold increased ventricular ejection fraction by 38% [56]. iPSC-CMs cultured on fibrillar scaffolds showed a 75% improvement in sarcomeric organization compared to two-dimensional cultures [59].

Decellularized extracellular matrix (dECM) scaffolds are used to support cellular adhesion and differentiation. Recellularizing scaffolds with stem cells represents a promising strategy for cardiac regeneration, but challenges remain regarding cellular maturation, vascularization, and the functional integration of regenerated tissue [60].

Recellularizing biomimetic scaffolds with iPSC-derived cardiomyocytes (iPSC-CMs) is an emerging direction in cardiovascular tissue engineering, with the potential to replace necrotic cardiac tissue and restore myocardial function [57].

iPSC-CMs offer unique advantages, such as the following:

• High immunological compatibility, as they can be generated from a patient’s somatic cells [54];
• High scalability, enabling the production of large numbers of cardiomyocytes for regenerative therapies [61];
• Use in pharmacological screening, as they serve as an ideal experimental model for toxicity testing [56].

However, the integration of iPSC-CMs into native tissues and their functional maturation remain significant challenges [59]. This section analyzes recellularization strategies based on iPSC-CMs, their advantages, and the obstacles that must be overcome for clinical application.

Differentiation methods include the embryoid body (EB) method, which allows the spontaneous formation of cardiomyocytes but results in high heterogeneity [62]. Additionally, the monolayer protocol based on Wnt activation/inhibition enables directed differentiation and the generation of cardiomyocytes with a mature phenotype [61], while 3D bioreactors enhance differentiation efficiency and provide biomimetic conditions [63].

In a study using electrospun scaffolds, iPSC-CMs demonstrated a 65% improvement in sarcomeric alignment and enhanced maturation [64]. To promote the survival and functionality of iPSC-CMs, biomimetic scaffolds based on ECM and biodegradable polymers are used [55]. Collagen- and fibrin-based scaffolds enhance cellular attachment and cardiomyocyte maturation [65]. Decellularized extracellular matrix (dECM) contains tissue-specific proteins that facilitate cell migration and maturation [25].

ECM-based cardiac scaffolds increased junctional protein expression by 80%, improving cellular cohesion and contractility [66]. Recellularizing scaffolds with iPSC-CMs offers an innovative solution for myocardial regeneration, but cellular maturation, post-transplant arrhythmias, and insufficient vascularization remain major challenges [59]. Future research should optimize electrical and biomechanical stimulation to enhance iPSC-CM maturity and functionality in recellularized tissues.

Regenerating cardiac tissue remains one of the greatest challenges in regenerative medicine, given the limited proliferative capacity of adult cardiomyocytes to repair myocardial injuries [52]. Recellularizing decellularized scaffolds with mesenchymal stem cells (MSCs), iPSC-derived cardiomyocytes (iPSC-CMs), and scaffold bioactivation represent innovative solutions for restoring cardiac function [53].

This section presents current recellularization strategies, the advantages and limitations of each method, and recent advances in scaffold bioactivation to improve cell adhesion, maturation, and integration into native tissues [54].

Recellularizing biomimetic scaffolds with stem cells and iPSC-derived cardiomyocytes (iPSC-CMs) is an innovative direction in myocardial regeneration, with the potential to restore heart function after myocardial infarction [52,57].

This approach involves the following:

• Using pluripotent (iPSC) and mesenchymal (MSC) stem cells to generate cardiomyocytes [54];
• Integrating iPSC-CMs into decellularized scaffolds to create a microenvironment conducive to regeneration [25];
• Bioactivating scaffolds by adding growth factors, bioactive peptides, and biostimulation [55].

The types of stem cells used in cardiovascular recellularization are:

• Embryonic stem cells (ESCs) have high differentiation potential but are associated with ethical and immunological concerns [56].
• Induced pluripotent stem cells (iPSCs) can be genetically customized, eliminating immune rejection risks, but exhibit incomplete maturation [57,61].
• Mesenchymal stem cells (MSCs) secrete paracrine factors (VEGF, TGF-β) that stimulate angiogenesis and cell recruitment [58].

Some experimental results are:

• MSCs grafted into a decellularized scaffold improved left ventricular function by 38% in animal models [66].
• iPSC-CMs cultured on fibrillar scaffolds showed a 75% improvement in sarcomeric organization and exhibited spontaneous contractility after 14 days [59].
• Biomimetic scaffolds based on ECM and biodegradable polymers are essential for creating an optimal microenvironment for iPSC-CMs [55].
• Cardiac ECM-based scaffolds increased junctional protein expression by 80%, improving cellular cohesion and contractility [65].
• Collagen- and fibrin-based scaffolds improved cardiomyocyte maturation by 60% compared to 2D cultures [25].

Recellularizing scaffolds with stem cells and iPSC-CMs represents a promising approach for myocardial regeneration. However, challenges related to cellular maturation, functional integration, and scaffold vascularization must be addressed by optimizing biostimulation and extracellular matrix architecture [60].

2.4. Bioreactors for Cardiac Maturation: Perfusion, Electrical, and Mechanical Stimulation

Bioreactors play a critical role in cardiovascular tissue engineering, providing controlled conditions for the maturation and functionalization of engineered cardiac constructs. These systems are designed to mimic key aspects of the cardiac microenvironment, utilizing perfusion, electrical stimulation, and mechanical stimulation to enhance tissue viability, organization, and contractility.

To structure the literature reviewed in this study, Figure 5 illustrates the distribution of selected studies based on the type of bioreactor investigated.

A relatively balanced distribution is observed among the three main types of stimulation, with electrical stimulation studies accounting for 38% of the selected references, reflecting the growing interest in replicating bioelectric signals essential for cardiac function. Perfusion systems (30%) have also been widely studied due to their crucial role in maintaining oxygen and nutrient transport to cells. Additionally, mechanical stimulation bioreactors (32%) are frequently used to replicate the natural biomechanical stresses of the myocardium, which are essential for the development of mature and functional cardiac tissue.

This classification does not reflect the overall distribution of literature in the field but rather the thematic structure of the references included in this analysis. In the following sections, each of these bioreactor types will be discussed in detail, highlighting their advantages, challenges, and applications in cardiac regeneration.

2.4.1. Perfusion Bioreactors for Cardiac Tissue Maturation

Cardiovascular tissue engineering is a crucial research direction in the development of regenerative therapies for patients with heart failure. One of the most advanced technologies in this field is perfusion bioreactors, which provide a controlled microenvironment for cardiomyocyte maturation and functional myocardial tissue development [67,68].

Perfusion in bioreactors mimics physiological blood flow, supplying oxygen, nutrients, and growth factors necessary for the differentiation and maturation of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) [69]. Furthermore, synchronized mechanical and electrical stimulation enhances the formation of an organized sarcomeric architecture, characteristic of mature myocardium [70,71].

A major advantage of perfusion-based bioreactors is their ability to enhance the distribution of cells within 3D scaffolds, reducing cellular hypoxia and promoting the formation of an internal vascular network [72]. Computational models indicate that optimizing perfusion flow rate and hydrostatic pressure can significantly impact cell viability and tissue organization [73].

An experimental study on iPSC-derived cardiomyocytes demonstrated that the use of a perfusion bioreactor with cyclic stimulation led to a 60% increase in contractile gene expression and improved electrophysiological coordination compared to static cultures [74]. Additionally, continuous perfusion helped maintain an optimal metabolic profile, reducing lactate production and enhancing glucose and fatty acid utilization [75].

Perfusion bioreactors can be further optimized by integrating advanced biosensors for non-invasive monitoring of tissue maturation. Recent studies show that measuring differential pressure in perfusion systems can provide insights into vascularization progress and cellular distribution within scaffolds [67,72].

Another emerging research direction is heart-on-chip perfusion bioreactors, which enable the real-time analysis of microstresses within engineered cardiac tissues. These models offer an intriguing insight into the relationships that exist among cardiomyocytes, ECM, and perfusion flow, and how the interactions of these elements improve the understanding of electrophysiological and mechanical maturation [70,71].

In cardiovascular tissue engineering, perfusion bioreactors are seen as a critical enabling technology as they provide a system whereby artificial myocardium is developed within an environment that is orthotopic to the body.

The combination of perfusion with electrical and mechanical stimulation alleviates the limitations of iPSC-derived cardiomyocytes’ (iPSC-CMs) enhanced sarcomeric organization, contraction, and expression of maturation markers [68,69]. Research efforts in the next phase should be directed toward developing individualized perfusion approaches and the incorporation of smart biomaterials to improve cell and tissue viability and maturation under physiological conditions.

2.4.2. Electrical Stimulation Bioreactors for Cardiac Tissue Engineering

The use of bioreactors with electrical stimulation represents a new frontier in cardiovascular tissue engineering, where the cells are given a microenvironmental control to enable differentiation and maturation of induced pluripotent stem cell cardiomyocytes (iPSC-CMs) [67,68]. The use of electric stimulation enhances electrophysiological maturity, contractile protein expression, and sarcomere formation [76,77,78].

Cardiomyocytes cultured in bioreactors can contract and respond to electrical signals. Exposing them to a specific electric field can develop properties that accurately mimic natural cardiac tissue. The studies proved that there is a remarkable rise in the level of expression of Cx43, the principal protein of gap junctions that synchronize contraction [79,80].

The utilization of electric stimulation applied within bioreactors for cardiac tissue maturation is a promising methodology for achieving the engineering of functional cardiac tissues. Some studies indicate that certain stimulation protocols can improve the organization of sarcomeres, increase markers of maturation, enhance synchronized contractility, and even reduce arrhythmias [81,82]. Further research is required in regard to optimizing stimulation regimens and combining them with other forms of biostimulation to implement these technologies clinically [83].

2.4.3. Mechanical Stimulation Bioreactors for Cardiac Tissue Maturation

The development of mechanical stimulation bioreactors is one of the most innovative approaches in tissue engineering, which aims to create cardiomyocytes with native-like electro-mechanical properties. It has recently been reported that a defined level of mechanical loading on iPSC-CMs can increase their sarcomeric maturation, cell proliferation, and the expression of major myocardial markers [69,70].

Bioreactors that provide for mechanical stimulation are central in cardiovascular tissue engineering because they aid in the maturation of cardiomyocytes and improve the architectural and functional properties of the engineered tissue [84].

The application of these technologies to cell cultures makes it possible to obtain tissues that are closer in structure and function to the adult myocardium, which is critical in responding to the needs of regenerative medicine [70,85,86]. In the future, studies should combine mechanical stimulation with electroactive biopolymers to improve the maturity and functional integration of the cardiac tissue-engineered constructs.

2.5. Challenges in Cardiovascular Tissue Engineering

The decellularization and recellularization process plays an important role in the engineering of cardiovascular tissues. These processes aim to develop a scaffold that is biomimetic and can support the actual regenerative function of cardiac tissues. A scaffold that has bioactivity and desired mechanical properties requires a careful balance between cellular component removal and ECM (extracellular matrix) integrity preservation [29,44].

The current methods of decellularization involve physical, chemical, and enzymatic agents. Each method has its own set of advantages and challenges. For example, ionic detergents like SDS manage to remove most cellular components. However, they tend to destroy critical ECM proteins that are necessary for cell adhesion and differentiation [87,88].

A major challenge lies in preserving ECM composition, particularly collagen, elastin, and proteoglycans, which are crucial for the biomechanical properties of the scaffold. Studies have shown that perfusion-based methods exert less structural disruption compared to immersion-based techniques; however, their efficacy in antigen removal remains limited [89]. Moreover, residual cellular debris may trigger post-implantation immune responses, affecting scaffold integration into the host tissue and reducing regenerative efficiency [90,91].

Recellularization involves repopulating the decellularized scaffold with stem cells, iPSC-derived cardiomyocytes, or cardiac progenitor cells. A significant challenge is the limited cell penetration and uneven distribution within the scaffold structure, particularly in dense or highly complex architectures [55,92]. To address these limitations, dynamic bioreactors have been employed to enhance cellular distribution by applying controlled perfusion and biomechanical stimulation, leading to improved cell retention and contractile marker expression [93].

Another critical aspect is the formation of a functional vascular network within the recellularized scaffold, necessary for metabolic support post-implantation. Studies suggest that prevascularizing scaffolds before implantation can accelerate cell integration and survival; however, current angiogenesis-inducing methods are still constrained by insufficient biochemical support and incomplete capillary network maturation [94]. In this context, incorporating pro-angiogenic growth factors and utilizing iPSC-derived endothelial cells may offer promising solutions.

Optimizing decellularization and recellularization protocols remains a key objective for advancing efficient regenerative therapies in cardiovascular tissue engineering. Future efforts should focus on enhancing decellularization methods to minimize ECM alterations, developing advanced recellularization strategies to ensure uniform cell distribution, and inducing a functional vascular network within engineered constructs [55,87,88].

Progress in biomaterials, advanced bioreactors, and genetic engineering will be crucial for creating functional, durable myocardial grafts capable of improving clinical outcomes in patients with severe heart failure. The biocompatibility of scaffolds and their in vivo immune response are fundamental factors in the success of cardiovascular tissue engineering therapies, influencing integration, functionality, and long-term stability in the host organism. Scaffolds used in cardiac regeneration must be compositionally optimized to prevent aggressive immune reactions, which could lead to fibrotic capsule formation and impaired functional integration [95,96]. Multiple parameters influence scaffold biocompatibility, according to experimental research, including material composition, porosity, crosslinking degree, and bioactive molecule content [97,98].

The host immunological response is a major predictor of scaffold integration success. Macrophages are essential in this process; M2 pro-regenerative polarization stimulates angiogenesis and tissue remodeling, while constant M1 inflammatory activation may cause fibrosis and early scaffold destruction [99,100].

Recent studies show that because of their native proteins that control immune activity, decellularized extracellular matrix (dECM)-based scaffolds produce a smaller inflammatory response than synthetic materials [101,102]. Moreover, affected by scaffold’s in vivo integration is its contact with immune cells and activation of pro-inflammatory signaling pathways. Biodegradable polymeric materials first cause an early inflammatory response in subcutaneous implantation models and then shift toward a pro-regenerative immunological profile [103,104].

Scaffold porosity plays a crucial role in host response, affecting cell recruitment, vascularization, and metabolic waste elimination. Scaffolds with controlled porosity and interconnected pores have shown reduced fibrotic capsule formation and improved cell infiltration and vascularization [96,97].

The degradation profile of scaffolds significantly influences biocompatibility. Degradation byproducts may trigger variable immune responses, and some materials may cause chronic inflammation if not properly resorbed. Studies on biodegradable polymeric scaffolds indicate that gradual degradation and controlled metabolite release minimize the inflammatory response, facilitating tissue regeneration without adverse effects [95,103]. Conversely, materials with inadequate degradation rates may lead to the accumulation of toxic residues, triggering excessive immune cell recruitment and compromising regeneration [99,104].

Scaffold biocompatibility and in vivo immune responses are critical factors in cardiovascular tissue engineering success. Materials must be optimized to maintain a balance between degradation, mechanical support, and cellular integration. Future research should focus on developing immunomodulatory scaffolds, which promote regenerative immune responses, reduce rejection risks, and enhance clinical applications for myocardial regeneration [98,100]. These advances will contribute to the development of effective cardiac regeneration strategies and the successful translation of engineered scaffolds into clinical applications.

The lack of reliable predictive models for tissue regeneration success remains a major challenge in cardiovascular tissue engineering. Interpatient variability, including age, comorbidities, immune response, and regenerative capacity, significantly impacts scaffold-based treatment outcomes, complicating the development of standardized strategies [105,106]. Additionally, scaffold heterogeneity, resulting from differences in ECM composition, porosity, stiffness, and biodegradability, affects host tissue integration and long-term functionality [107,108].

Currently, most preclinical studies rely on animal models, which fail to capture the full complexity of human tissue regeneration. In vitro cell line testing lacks biological variability, while in vivo animal models do not always replicate human immune-scaffold interactions [34,104]. This creates a gap between preclinical findings and clinical scaffold efficacy, hindering clinical translation [101,103]. A critical limitation in cardiovascular tissue engineering is the lack of robust computational models capable of simulating regeneration processes based on patient-specific characteristics and scaffold properties [96,99]. Current tissue regeneration models often fail to fully integrate key variables such as immune cell interactions, extracellular matrix dynamics, and biomechanical factors. Furthermore, new research shows that artificial intelligence (AI) and deep learning (DL) can be used to evaluate vast preclinical and clinical datasets, enhancing the capacity to forecast the success of scaffold implantation [97,102].

The capacity of scaffolds to meet patient-specific requirements presents another major obstacle. Variability in myocardial tissue structure and ECM composition can influence scaffold integration and the host immune response [98,108]. Currently, scaffold personalization strategies include the use of 3D bioprinting to create patient-specific structures based on imaging data and adaptive hydrogels capable of responding to local stimuli [105,106]. Additionally, immune cell-scaffold interactions are difficult to predict due to the variability of the immune system among patients. Synthetic scaffolds may trigger excessive inflammatory responses, whereas ECM-based scaffolds may exhibit unpredictable degradation depending on patient-specific enzymatic activity [100,107].

In conclusion, the lack of effective predictive models remains a major obstacle in cardiovascular tissue regeneration. Patient and scaffold variability necessitate the development of advanced personalization strategies, the use of artificial intelligence for clinical data analysis, and the optimization of scaffolds to minimize immunological risks and rejection. Future research should focus on the development of integrated predictive models capable of anticipating individual treatment responses, facilitating the transition from experimental research to effective clinical applications [99,109].

3. Deep Learning in Tissue Engineering: A New Frontier

To highlight the evolution of research on the application of deep learning in tissue engineering, Figure 6 illustrates the distribution of articles used in the following two chapters based on their year of publication. This distribution emphasizes the growing scientific interest in utilizing deep learning techniques for scaffold optimization, modeling regeneration processes, and personalizing data-driven therapies.

Figure 7 illustrates the distribution of key research areas where deep learning is applied in cardiovascular tissue engineering. The majority of studies (40%) focus on the application of DL in cardiac tissue regeneration, followed by its use in cardiovascular imaging and modeling (27%). Approximately 20% of research explores the optimization of machine learning algorithms for tissue engineering, while 13% investigates emerging AI technologies, such as 3D bioprinting and process automation.

The number of works increased from 2 per year in 2017–2019 up to 11 in 2021 and 2023, which confirms that AI is used more and more in this area. This classification provides an overview of how artificial intelligence contributes to the development of new solutions in regenerative cardiovascular medicine.

3.1. How Can Deep Learning Support Cardiac Regeneration?

The past few years have seen the emergence of artificial intelligence that has made deep learning models that can be applied to biomedical processes such as tissue regeneration. For instance, the use of deep learning algorithms in histological and microstructural imaging analysis has become vital in the classification of scaffold materials in tissue engineering. This guarantees accuracy in scaffold architecture design, hence enhancing processes of tissue maturation and recellularization [34].

In addition to optimizing scaffolds and regenerative protocols, deep learning plays a critical role in the broader patient journey. Convolutional neural networks (CNNs) are increasingly used to analyze and segment cardiac MRI, CT, and histological images with high precision. These models support early diagnosis, identify subtle tissue abnormalities, and quantify fibrosis or scar formation. Moreover, deep learning enables real-time monitoring of post-implantation tissue evolution through imaging, predicting outcomes based on patient-specific variables. This clinical integration of DL can help personalize treatment plans, improve follow-up strategies, and reduce the risk of graft failure. Based on our doctoral research and ongoing work in this area, we aim to highlight and expand this underrepresented aspect in future studies.

Deep learning offers several advantages in studying heart regeneration scaffolds with convolutional neural networks (CNNs), which identify advanced microstructural characteristics. These methods allow scaffolds to be arranged for varied uses by composition and porosity as well [110]. Furthermore, knowledge of cell-ECM interactions is essential for the grasp of cell adhesion and proliferation [111], and automated histological change detection in decellularized tissue samples greatly reduces human interpretation bias [112].

One of the most often utilized instruments in the study of scaffolds is scanning electron microscopy (SEM), so deep learning (DL) algorithms can be taught to read images to grasp the material structure and porosity with more precision [113]. Moreover, deep learning models can identify minute structural elements that might compromise the biological fit of a scaffold using stem cells or iPSC-derived cardiomyocytes [114].

Studies employing generative adversarial networks (GANs) and convolutional neural networks have shown that scaffold classification accuracy could be better than 90 percent, therefore accelerating the validation of the structure for clinical usage [115].

A step toward customized therapy and enhanced regenerative medicine is using a deep learning method to scaffold evaluation in stem cell cardiac tissue regeneration.

Nonetheless, further work is necessary to test these algorithms on actual clinical data and define uniform criteria for the analysis [110]. The application of deep learning and big data phenomena in the tissue engineering of the heart markedly improves the scaffold design for cardiac regeneration.

The primary focus is on predicting scaffold assimilation into the patient’s body from analysis of intricate clinical, histological, and biomechanical datasets to improve painless acceptance [34].

Big data includes the acquisition and analysis of massive datasets from sources such as medical images, biomechanics, patient DNA, and scaffold characteristics. These datasets serve as a basis to create models that predict the chances of implant acceptance and minimize the chances of rejection or failure [116].

An MLATE model serves as an example of the application of deep learning in tissue engineering. The main idea of the model is to predict tissue integration based on cellular behavior over the constructs positive for cardiovascular tissues [117].

Such approaches allow tailoring of the regenerative therapies through the exact specification of scaffold materials and mitigation of immunological problems [118].

Before implantation, scaffold simulation and optimization are possible thanks to deep learning and big data models [110]. Cardiovascular tissues, their biochemical microenvironments, and even the immune system are modeled using Convolutional (CNN) and Recurrent (RNN) neural networks [50].

In addition, predictive models can identify patients with the most promising scaffold integration chances based on tissue histology, biomechanical features, and inflammation markers [119]. These methods are already used in cardiovascular medicine for disease diagnosis and even for the more challenging task of rejuvenation therapy optimization [120].

One of the most striking features of the use of big data in regenerative medicine is the precise personalization of scaffolds for every patient. It is possible to optimize scaffold biocompatibility, porosity, and durability, ensuring maximal integration [121].

The prediction of scaffold degradation over time is another scope of deep learning, which in turn helps in selecting the best-suited materials for each patient [122]. Recent studies indicate that big data and AI can enhance the selection of scaffold materials by decreasing the failure rate and increasing the lifespan of implants [123].

Thus, the integration of big data and deep learning in cardiovascular regenerative medicine enhances scaffold optimization, personalizes patient therapies, and improves the success rate of regenerative treatments [124]. These technological advancements contribute to the development of high-precision predictive models for advanced cardiac regeneration strategies.

Table 2. Overview of applications, advantages, and limitations.

Deep Learning Technique	Application in Scaffold Integration Prediction	Advantages	Limitations	Studies & References
CNN (Convolutional Neural Networks)	Analysis of histological and microstructural images to assess scaffold integration.	High accuracy in pattern recognition.	Requires large, well-annotated datasets.	[34,117,119,123]
RNN (Recurrent Neural Networks)	Predicting scaffold behavior based on patient evolution (time-series models).	Captures temporal dependencies between patient data and scaffold performance.	Prone to vanishing gradient issues.	[119,122,124,125]
GANs (Generative Adversarial Networks)	Synthetic generation of scaffold structures for simulation and in silico validation.	Enables realistic simulations for scaffold optimization.	Risk of mode collapse and overfitting.	[120,121,123]
Autoencoders	Detecting anomalies in scaffold integration and tissue remodeling.	Identifies micro-failures in scaffold adaptation.	Needs high-quality, balanced training data.	[118,124,125]
Hybrid CNN + LSTM	Combining spatial and temporal analysis for scaffold evolution in vivo.	Merges visual and sequential analysis for more precise modeling.	Requires significant computational power.	[50,116,124]
Graph Neural Networks (GNNs)	Modeling molecular and cellular interactions affecting scaffold integration.	Maps complex biological relationships.	High model complexity, requires domain-specific datasets.	[117,119,120]
Explainable AI (XAI) in Deep Learning	Enhancing clinician trust in AI-driven scaffold prediction models.	Provides interpretability for scaffold evaluation decisions.	Still in early research stages for tissue engineering.	[121,122,125]

summarizes the main deep learning techniques used for predicting scaffold integration in patients, highlighting specific applications, advantages, and limitations of each method. By incorporating big data-driven predictive models, these techniques enable a precise assessment of scaffold biocompatibility, optimization of the recellularization process, and personalization of tissue regeneration strategies. Each method is supported by recent studies, demonstrating advancements in cardiovascular tissue engineering and their clinical applicability.

3.2. Recent Applications

Artificial intelligence (AI) has grown fast in recent years; deep learning (DL) is a breakthrough tool in biomedical data processing. Particularly, tissue bioengineering and cardiology have profited much from developments in neural network algorithms, allowing the processing of complicated medical pictures, tissue biomechanical behavior prediction, and cellular-level cardiac regeneration modeling.

This section reviews recent literature on the application of DL in tissue bioengineering and cardiology, focusing on cutting-edge studies, methodologies, clinical research impact, and current technological limitations. One of the most significant applications of DL in cardiology is the enhancement of medical imaging, where convolutional neural networks (CNNs) have transformed cardiac tissue diagnosis and analysis [126,127,128,129,130].

Sermesant and his team demonstrated the use of DL in analyzing cardiovascular magnetic resonance imaging (CMR) and cardiac computed tomography (CT), significantly improving the automatic detection of structural abnormalities [131]. Their work used CNN models trained on massive databases of cardiac pictures, able to autonomously separate structures, including the myocardium and ventricles, with an accuracy matching human specialists.

In a related work, Wang and associates used DL for tomographic image reconstruction—a necessary step toward coronary stenosis diagnosis [132]. Generative adversarial networks (GANs) were used by researchers to improve the quality of cardiac pictures produced by CT and MRI, greatly lowering artifacts and noise. In this work, deep learning models were used for tomographic image reconstruction as a step toward improving coronary stenosis diagnosis. Specifically, they applied GANs to cardiac CT and MRI images, resulting in enhanced image quality and visibly reduced artifacts. While the original study does not provide a universal SNR improvement value, it references measurable gains such as peak signal-to-noise ratio (PSNR) improvements of 4–6 dB in low-dose CT, depending on the specific dataset and reconstruction task. These results indicate that DL methods can substantially reduce image noise and preserve anatomical fidelity.

Personalized therapy depends on these developments since they let doctors see heart tissue in great detail, spot minor structural changes in the myocardium, and maximize treatments depending on a very accurate diagnosis.

An area of developing interest where DL has shown remarkable prediction power is cardiovascular biomechanics. Blood flow dynamics is modeled using artificial intelligence algorithms; cardiac tissue activity is predicted; and atherosclerosis and myocardial infarction risk are evaluated. Using convolutional and recurrent neural networks, Arzani and his colleagues combined DL with sophisticated numerical techniques to forecast blood flow in coronary arteries [133]. Achieving simulation rates ten times faster than standard computational fluid dynamics (CFD), their investigation revealed that DL models might predict hemodynamic changes linked with coronary stenosis. Arzani and associates discussed the use of DL models to predict complex hemodynamic parameters relevant to coronary stenosis, particularly in the context of fluid–structure interaction and patient-specific vascular geometry. While their paper highlights the potential of DL in capturing nonlinear biomechanical patterns, it does not provide a specific predictive model or accuracy results. Instead, the authors emphasize the need for future research that combines high-resolution data with explainable ML techniques to reliably model vascular behavior.

In another work, Lu and colleagues investigated DL applications in atherosclerosis diagnosis, building a neural network architecture adept at automatically recognizing atherosclerotic plaques and evaluating myocardial infarction risk based on artery wall imaging properties [134]. This work reviewed multiple deep learning models used in the diagnosis of coronary atherosclerotic heart disease. Their paper summarizes the use of various ANN architectures, such as convolutional neural networks (CNNs), VGG16, and ResNet, in tasks like plaque detection and risk stratification. Although the authors do not present an original model, they cite studies where CNN-based architectures achieved diagnostic accuracies of up to 92.8% in identifying atherosclerotic plaques. However, they do not provide a specific ANN model or quantitative probability for myocardial infarction prediction, as their work focuses on compiling findings from prior studies.

These uses show how well DL may prevent cardiovascular diseases, which helps doctors to spot high-risk patients and customize their treatment plans.

Using DL in tissue engineering—especially in cardiac tissue regeneration and the creation of sophisticated biomaterials—showcases a bright future for the discipline.

Wu and associates showed that DL could maximize the 3D bioprinting process for cardiac tissue, therefore enabling the construction of structures with better biomechanical characteristics [135]. Their algorithms were taught on cellular imaging datasets, which allowed bioink composition to be automatically adjusted to maximize cardiac stem cell viability.

Moreover, Kalkunte et al. created a DL-based platform to improve cardiac stem cell phenotyping, applied it for controlled differentiation of stem cells into useful cardiomyocytes [136].

This technology could enable the creation of personalized cardiac tissue grafts, with major implications for heart failure treatment.

Despite the remarkable progress of DL in cardiology and tissue bioengineering, several technical and ethical challenges remain, including the need for larger and more diverse datasets to train DL models, the lack of interpretability of these models in clinical medicine, and the integration of AI into medical practice to gain acceptance from healthcare professionals.

In conclusion, the application of DL in tissue bioengineering and cardiology has transformative potential, and future research in this field will be marked by continuous innovations in medical image analysis, biomechanics modeling, and cardiac tissue regeneration.

4. Integrating Deep Learning into Tissue Engineering Processes

Artificial intelligence (AI) and deep learning (DL) have become essential tools in tissue engineering, significantly contributing to the development of innovative solutions for tissue regeneration.

Figure 8 illustrates the distribution of key research directions in this field, highlighting four major areas: AI-driven scaffold design (37%), personalized regenerative medicine (32%), computational modeling for tissue engineering (21%), and deep learning applications in biomedical research (10%).

This chapter will further explore the role of AI in scaffold design and its applications in personalized regenerative medicine, highlighting the latest studies and technological advancements in this field.

4.1. AI-Driven Optimization of Scaffold Design

Identifying the ideal composition of the extracellular matrix (ECM) for transplantation remains one of the greatest challenges in tissue engineering. ECM composition directly influences cell adhesion, proliferation, and differentiation, and its optimization requires analyzing a wide range of biochemical and mechanical variables. Deep learning models have proven highly effective, providing the ability to correlate extensive experimental data and identify optimal biomaterial combinations [137,138].

Recent work showed that convolutional neural networks (CNNs) can examine experimental data and histology pictures to identify trends in cell-ECM interactions within synthetic scaffolds [34]. This method allows the exact change in the ratio between structural proteins (collagen, elastin, fibronectin) and bioactive components (growth factors, glycosaminoglycans) to generate scaffolds that quite closely reflect the natural ECM.

Apart from CNNs, generative adversarial networks (GANs) are being applied to replicate tailored ECM designs. Researchers used GANs in a newly published work to create virtual scaffolds depending on data obtained from natural tissues [135]. Successful scaffold structure optimization by the trained models maximizes cell colonization and preserves consistent nutrition delivery. This is a very important avenue for regenerative medicine since it allows the creation of completely integrated tissue architectures with biomechanical characteristics like those of natural tissues.

Furthermore, variational autoencoders (VAEs) to find latent scaffold traits possibly affecting transplanting success. These models let 3D architecture and chemical composition be optimized simultaneously. By means of their microstructure, VAEs were found to be able to forecast the mechanical behavior of scaffolds, therefore offering more control over material stiffness and degradation rates [139].

The integration of reinforcement learning (RL) for the optimization of biomimetic and self-organizing scaffolds is another developing discipline. Based on real-time biological data, RL models help scaffold properties to be dynamically regulated. An RL algorithm was learned, for example, to modify scaffold porosity in articular cartilage regeneration, thereby improving nutrition flow and cell infiltration [119].

Originally designed for natural language processing, Transformer models have been modified for scaffold analysis and yield quite precise estimates of biochemical interactions between cells and biomaterials. Transformer models have been shown recently to be able to predict which of several ECM compositions will show the best success rates for cellular integration by analyzing their molecular structure [140].

The use of DL to examine tissue remodeling following scaffold implantation is still another exciting avenue for study. Currently used to track real-time structural changes in scaffolds and modify their composition depending on host response are Long Short-Term Memory (LSTM)-based models [141]. By allowing the creation of intelligent scaffolds able to dynamically adapt to local conditions in the body, this technology has the power to transform the field. Researchers are also investigating the use of multi-agent systems grounded on DL for self-organizing scaffolds, which replicate natural cell activity during embryonic development. These models examine stem cell-ECM interactions, therefore enabling the construction of scaffolds that naturally generate three-dimensional structures without depending on a specified rigid architecture [142].

All of these developments point to deep learning’s vital contribution to transforming tissue engineering, therefore enabling the creation of more effective, customized, and flexible constructions. Scaffolds used in regenerative medicine are predicted to become more integrated with natural biological processes as DL models become more complex and widely available, offering tailored solutions for patients with degenerative diseases or post-traumatic injuries.

4.2. Personalized Treatment Approaches in Regenerative Medicine

With direct applications in regenerative medicine, the personalizing of therapies in AI-assisted tissue engineering has grown to be a critical focus of research. Predicting patient responses to scaffold implantation, in which machine learning (ML) and deep learning (DL) enable the study of complex datasets encompassing genetic, histological, imaging, and biomechanical information, presents a key difficulty. These technologies not only maximize the choice of scaffolding but also offer real-time treatment modifications employing constant monitoring of tissue regeneration.

Deep learning models taught on large-scale databases have been shown in recent studies to be able to examine tissue regeneration patterns and find patients very likely to have excellent scaffold integration. Using variational autoencoders (VAEs) and convolutional neural networks (CNNs) to examine histological pictures and transcriptome data produces quite accurate predictions of cellular responses to different biomaterials [143]. By changing scaffold composition based on the biological traits of the patient, these models allow a tailored approach to tissue regeneration. Post-implantation scaffold monitoring has advanced considerably thanks to artificial intelligence inclusion into medical imaging analysis. To examine CT and MRI images, researchers have created CNN- and recurrent neural network (RNN)-based models tracking in real-time processes, including vascularization, inflammation, and tissue remodeling [144]. These models have shown better predictive powers than more traditional approaches, therefore identifying post-implant problems before clinical symptoms start. For instance, Tang et al. developed a machine learning model that fused intra-atrial and body surface electrocardiographic data to predict atrial fibrillation recurrence following catheter ablation. Their ML model achieved an area under the ROC curve (AUC) of 0.82, while the commonly used CAAP-AF score reached only 0.61, illustrating a marked enhancement in early risk prediction and personalized patient management.

Moreover, developments in molecular modeling have produced methods based on genomics for personalizing. Analyzing gene expression patterns connected to osteogenesis and angiogenesis, recent research has investigated the application of artificial intelligence to find patients with a hereditary inclination for faster regeneration [145]. Using this data, AI-driven predictive algorithms suggest customized scaffolds, therefore optimizing material structure and growth factor release depending on the particular demands of the patient.

Scaffold customization also depends critically on the biomechanical analysis of material-cell interactions. By simulating scaffold mechanical behavior based on patient characteristics, machine learning algorithms have been used to fine-tune material parameters, thereby guaranteeing optimal integration [146]. Compared to conventional materials, studies show that artificial intelligence-optimized scaffolds show better success rates in cell proliferation and long-term stability. The prevention of problems and treatment modification in regenerative medicine depend on scaffold monitoring following implantation. Deep learning models based on Transformers and LSTMs examine data from medical imaging, biochemical indicators, and clinical evaluations, allowing dynamic scaffold composition changes and treatment alterations depending on patient progress [147]. This real-time adaptation technology guarantees the best regeneration and lowers the implant failure risk.

Another developing discipline is ECM-level cell interaction analysis employing artificial intelligence models. Deep learning has been shown by researchers to evaluate ECM molecular structure and spot cell-stem interactions with biomimetic scaffolds, therefore enhancing material design for tissue engineering [148]. This method minimizes inflammatory or fibrotic reactions and helps to create scaffolds for tissue regeneration.

All things considered, artificial intelligence-driven scaffold personalization is expanding opportunities in regenerative medicine. Using large histology, imaging, and genomic data analysis made possible by artificial intelligence, patient selection and material optimization are enhanced. Tissue engineering is predicted to attain hitherto unheard-of degrees of accuracy as machine learning technologies develop, where every scaffold is created and modified based on the unique traits of the individual patient. These developments not only improve clinical results but also help to move to customized treatment, therefore lowering complication risks and optimizing the effectiveness of regenerative therapies.

AI has evolved into a necessary instrument for combining histology and genetic data into predictive models applied in regenerative medicine in recent years. Advanced deep learning algorithms, which can examine complicated multi-omic datasets and find minor correlations between patient genetic structure and their responsiveness to tissue therapies, have enabled this breakthrough.

One basic feature of this technique is the transcriptome analysis using neural networks. Recent research shows that AI models can detect genetic biomarkers linked with efficient tissue regeneration, therefore enabling scaffold customisation depending on the biological profile of the patient [149]. This approach guarantees not only a more exact choice of biomimetic materials but also dynamic scaffold changes depending on real-time tissue regeneration development.

Moreover, stem cell selection for regenerative treatments has been transformed by single-cell RNA sequencing (scRNA-seq) studies in concert with sophisticated artificial intelligence algorithms. Using stem cell subpopulation identification with the best regenerative potential, deep learning algorithms trained on scRNA-seq data can optimize cell choice for tissue regeneration [150]. This method has important ramifications for tailored therapy since it allows scaffold modifications depending on the transcriptome profile of every patient.

Deep learning’s application in histological image analysis to enhance scaffold monitoring following implantation marks is still another significant development. Greater accuracy than traditional approaches has been demonstrated by recently created CNN models trained on high-resolution microscopic pictures identifying predicted patterns of regeneration success [33]. These artificial intelligence models allow scaffold adjustments depending on detected histological changes, therefore promoting more efficient regeneration and lowering of fibrosis risk. Bagherpour et al. reviewed various AI-based strategies for optimizing scaffold architecture and mechanical performance. Among the referenced works, CNNs were used to analyze micro-CT and SEM images of scaffolds, achieving classification accuracies above 90% for detecting porosity and fiber alignment defects. Generative models such as GANs were also discussed for their potential in simulating scaffold morphology and predicting in vitro cell responses. In the context of bioreactor regulation and tissue maturation, Bagherpour and team highlighted machine learning algorithms that adjusted perfusion rates and biochemical gradients in real time. Although specific AUC or RMSE values were not always provided, several of the cited models demonstrated improved efficiency in predicting tissue growth dynamics compared to fixed-parameter methods.

Beyond histology and genetic study, artificial intelligence models are currently being applied to maximize scaffold biomechanical qualities, hence advancing tissue engineering. Recent research shows that using patient-specific physiological characteristics, neural networks can forecast how scaffolds will interact with host tissue, thereby guiding material composition and scaffold structure [151]. The development of completely customized treatments that satisfy the particular demands of every patient depends on these developments.

Furthermore, a possible way to lower rejection risks and post-operative difficulties is the application of artificial intelligence in simulating immunological reactions to implants. By analyzing biomaterial-immune system interactions, machine learning and deep learning algorithms may generate quite accurate forecasts of whether an excessive inflammatory response or effective scaffold integration will take place [32]. Personalized medicine depends on this invention to guarantee the best material choice for every patient.

Ultimately, major progress in regenerative medicine comes from including histology and genetic data in AI-driven prognostic models. These technologies enable more precise scaffold personalization, detailed regeneration monitoring, and reduced complication risks, transforming tissue engineering into a field that is increasingly tailored to individual patient needs.

5. Challenges and Limitations of Deep Learning in Tissue Engineering

The integration of deep learning (DL) into cardiovascular tissue engineering presents remarkable opportunities; however, its widespread adoption is hindered by several major challenges. These include the following:

• Limited access to large, well-annotated datasets—The performance of DL algorithms is highly dependent on the volume and quality of training data. In cardiovascular tissue engineering, access to such data is restricted due to several factors:

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  Limited availability of tissue samples, as bioengineered tissues are obtained through advanced recellularization and 3D bioprinting techniques, and relevant experimental data are scattered across specialized laboratories.

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  Complex and subjective data annotation, requiring expertise in cell biology and cardiovascular pathology, making the process costly and time-consuming.

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  Data heterogeneity, resulting from variability in decellularization, recellularization, and bioreactor methods, making it difficult to develop generalizable DL models.

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  Potential solutions include establishing international consortia for data collection and standardization, developing public databases, and implementing semi-supervised learning techniques to reduce reliance on manual labeling.

• Need for interpretable models for clinical application—Deep learning models are often regarded as “black boxes”, which limits their acceptance by medical professionals. In cardiovascular tissue engineering, interpretability is crucial because of the following:

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  Clinicians must understand how and why an AI model recommends specific recellularization protocols or scaffold optimizations.

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  Regulatory bodies require scientific justification and auditing of AI models before they can be implemented in regenerative medicine.

• Ethical and regulatory barriers to AI use in regenerative medicine, raising several concerns:

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  Patient data protection is in compliance with GDPR and HIPAA regulations, ensuring confidentiality and data security.

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  Human oversight in AI-driven decisions, preventing errors that could impact patient safety.

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  Clear definition of legal responsibility in cases where AI-driven recellularization protocols fail to ensure scaffold integration.

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  Close collaboration among AI experts, biomedical researchers, and regulatory authorities is necessary to develop a transparent ethical framework for AI use in tissue engineering.

• High costs and implementation complexity, requiring significant investments in infrastructure and computational resources:

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  Training DL models requires high-performance processors (GPUs or TPUs), which may be unaffordable for smaller research laboratories.

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  Generating experimental data involves costly cell cultures, bioreactor tests, and histological analyses.

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  Integrating AI into biomedical workflows is challenging due to a lack of AI expertise in most research centers.

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  Potential solutions include leveraging transfer learning, cloud computing for distributed processing, and developing open-source AI platforms specifically for tissue engineering applications.

• Challenges specific to AI-driven scaffold and bioreactor optimization:

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  Optimizing biomimetic scaffolds by predicting mechanical and biochemical behavior requires extensive experimental validation.

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  Simulating tissue maturation in bioreactors involves modeling cellular responses to mechanical and electrical stimuli, yet generalizing these models across different scaffold types remains a challenge.

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  Researchers are exploring multimodal learning algorithms, which integrate microscopic imaging with experimental data, improving predictive accuracy for tissue regeneration.

Deep learning has the potential to revolutionize cardiovascular tissue engineering, but its effective integration requires addressing several critical challenges. Advancements in data collection and standardization, the development of interpretable models, adaptation of ethical regulations, and cost-reduction strategies are key priorities. Interdisciplinary collaboration among AI specialists, cell biologists, and tissue engineers will be essential for the successful adoption of this technology in clinical practice.

6. Future Directions and Conclusions

The application of deep learning (DL) in cardiovascular tissue engineering is an emerging field with significant potential to transform regenerative medicine. Although technology has advanced remarkably, fully using these solutions would need overcoming important obstacles such as AI model optimization, access to big and standardized datasets, and strengthening of multidisciplinary cooperation.

The advancement of this discipline depends fundamentally on cooperation among specialists in artificial intelligence, tissue engineering, and biology. While biomaterials and bioreactor engineers provide the best scaffolds and systems for tissue maturation, cell biology and regenerative medicine researchers offer knowledge on cell proliferation and differentiation mechanisms. Conversely, experts in artificial intelligence create algorithms that are able to examine large-scale data and provide exact forecasts on the effectiveness of regenerative therapies. By means of their synergy, several disciplines will help to integrate artificial intelligence into tissue engineering, thus bridging the gap between basic research and therapeutic uses.

One of the key obstacles is still restricted access to big, well-organized datasets, which are necessary to raise the performance of DL methods. It is necessary to create thorough databases with thorough knowledge of tissue maturation techniques, recellularization, and decellularization. Adopting interoperable formats and standardizing these data will allow artificial intelligence models to train on many datasets, therefore reflecting the complexity of heart regeneration. Furthermore, self-supervised and semi-supervised learning methods could speed the creation of more effective algorithms by relying less on hand annotation.

Furthermore, offering a more thorough understanding of tissue behavior during regeneration involves combining artificial intelligence with cutting-edge imaging technologies and genomic sequencing. Using artificial intelligence to create and optimize bioartificial organs is among the most exciting directions of research. By use of architectural configurations that improve cell proliferation and foster the development of functional tissue structures, AI models can be used to replicate and augment scaffold constructions. Moreover, DL techniques can direct the 3D bioprinting process by dynamically changing manufacturing settings to recreate native tissue architecture. Preclinical experimental data analysis by artificial intelligence could guide bioreactors’ ideal tissue maturation settings. Integrating these technologies could help to create completely working bioartificial organs, thereby substituting for traditional transplants and reducing donor shortages. New angles for individualized treatment and the creation of exact regeneration therapies surface as artificial intelligence technology progresses. Based on individual patient traits, DL-based predictive models can assist in modifying tissue stimulation techniques and recellularization approaches. AI can also examine epigenetic and genetic profiles to pinpoint ideal combinations of growth factors and stem cells required for cardiac regeneration. Through AI algorithms, pharmacological screening and tailored testing of cell therapies could hasten the identification of successful treatments and lower undesirable effects. Moreover, artificial intelligence might be applied for post-implant patient monitoring, imaging, and data analysis of biomedical equipment to identify possible issues.

Deep learning is, all things considered, a revolutionary tool for cardiovascular tissue engineering, but its successful integration into regenerative medicine depends on continuous improvement in artificial intelligence model optimization, the creation of vast and standardized databases, and strong multidisciplinary cooperation. AI can redefine customized medicine and enable the move of tissue regeneration from an experimental level to clinical uses when these research directions change.