Bioengineering

15 pages, 2779 KiB

Open AccessArticle

Automatic 3D Tracking of Liver Metastases: Follow-Up Assessment of Cancer Patients in Contrast-Enhanced MRI

by Sophia Schulze-Weddige, Uli Fehrenbach, Johannes Kolck, Richard Ruppel, Georg Lukas Baumgärtner, Maximilian Lindholz, Isabel Theresa Schobert, Anna-Maria Haack, Henning Jann, Martina Mogl, Dominik Geisel, Bertram Wiedenmann and Tobias Penzkofer

Bioengineering 2025, 12(8), 874; https://doi.org/10.3390/bioengineering12080874 (registering DOI) - 12 Aug 2025

Abstract

Background: Tracking differential growth of secondary liver metastases is important for early detection of progression but remains challenging due to variable tumor growth rates. We aimed to automate accurate, consistent, and efficient longitudinal monitoring. Methods: We developed an automatic 3D segmentation and tracking [...] Read more.

Background: Tracking differential growth of secondary liver metastases is important for early detection of progression but remains challenging due to variable tumor growth rates. We aimed to automate accurate, consistent, and efficient longitudinal monitoring. Methods: We developed an automatic 3D segmentation and tracking algorithm to quantify differential growth, tested on contrast-enhanced MRI follow-ups of patients with neuroendocrine liver metastases (NELMs). The output was integrated into a decision support tool to distinguish between progressive disease, stable disease, and partial/complete response. A user study involving an expert group of seven expert radiologists evaluated its impact. Group comparisons used the Friedman test with post hoc analyses. Results: Our algorithm detected 991 metastases in 30 patients: 13% new, 30% progressive, 18% stable, and 18% regressive; the remainder were either too small to measure (15%) or merged with another metastasis in the follow-up assessment (6%). Diagnostic accuracy improved with additional information on hepatic tumor load and differential growth, albeit not significantly (p = 0.72). The diagnosis time increased (p < 0.001). All radiologists found the method useful and expressed a desire to integrate it in existing diagnostic tools. Conclusions: We automated segmentation and quantification of individual NELMs, enabling comprehensive longitudinal analysis of differential tumor growth with the potential to enhance clinical decision-making. Full article

(This article belongs to the Special Issue AI-Driven Imaging and Analysis for Biomedical Applications)

16 pages, 1932 KiB

Open AccessArticle

2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET/CT

by Fei Liu, Wen Chen, Jianping Zhang, Jianling Zou, Bingxin Gu, Hongxing Yang, Silong Hu, Xiaosheng Liu and Shaoli Song

Bioengineering 2025, 12(8), 873; https://doi.org/10.3390/bioengineering12080873 (registering DOI) - 12 Aug 2025

Abstract

We aimed to establish non-invasive diagnostic models comparable to pathology testing and explore reliable digital imaging biomarkers to classify diffuse large B-cell lymphoma (DLBCL) and invasive ductal carcinoma (IDC). Our study enrolled 386 breast nodules from 279 patients with DLBCL and IDC, which [...] Read more.

We aimed to establish non-invasive diagnostic models comparable to pathology testing and explore reliable digital imaging biomarkers to classify diffuse large B-cell lymphoma (DLBCL) and invasive ductal carcinoma (IDC). Our study enrolled 386 breast nodules from 279 patients with DLBCL and IDC, which were pathologically confirmed and underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) examination. Patients from two centers were separated into internal and external cohorts. Notably, we introduced 2.5D deep learning and machine learning to extract features, develop models, and discover biomarkers. Performances were assessed using the area under curve (AUC) and confusion matrix. Additionally, the Shapley additive explanation (SHAP) and local interpretable model-agnostic explanations (LIME) techniques were employed to interpret the model. On the internal cohort, the optimal model PT_TDC_SVM achieved an accuracy of 0.980 (95% confidence interval (CI): 0.957–0.991) and an AUC of 0.992 (95% CI: 0.946–0.998), surpassing the other models. On the external cohort, the accuracy was 0.975 (95% CI: 0.913–0.993) and the AUC was 0.996 (95% CI: 0.972–0.999). The optimal imaging biomarker PET_LBP-2D_gldm_DependenceEntropy demonstrated an average accuracy of 0.923/0.937 on internal/external testing. Our study presented an innovative automated model for DLBCL and IDC, identifying reliable digital imaging biomarkers with significant potential. Full article

(This article belongs to the Section Biosignal Processing)

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21 pages, 5025 KiB

Open AccessArticle

Cascaded Self-Supervision to Advance Cardiac MRI Segmentation in Low-Data Regimes

by Martin Urschler, Elisabeth Rechberger, Franz Thaler and Darko Štern

Bioengineering 2025, 12(8), 872; https://doi.org/10.3390/bioengineering12080872 (registering DOI) - 12 Aug 2025

Abstract

Deep learning has shown remarkable success in medical image analysis over the last decade; however, many contributions focused on supervised methods which learn exclusively from labeled training samples. Acquiring expert-level annotations in large quantities is time-consuming and costly, even more so in medical [...] Read more.

Deep learning has shown remarkable success in medical image analysis over the last decade; however, many contributions focused on supervised methods which learn exclusively from labeled training samples. Acquiring expert-level annotations in large quantities is time-consuming and costly, even more so in medical image segmentation, where annotations are required on a pixel level and often in 3D. As a result, available labeled training data and consequently performance is often limited. Frequently, however, additional unlabeled data are available and can be readily integrated into model training, paving the way for semi- or self-supervised learning (SSL). In this work, we investigate popular SSL strategies in more detail, namely Transformation Consistency, Student–Teacher and Pseudo-Labeling, as well as exhaustive combinations thereof. We comprehensively evaluate these methods on two 2D and 3D cardiac Magnetic Resonance datasets (ACDC, MMWHS) for which several different multi-compartment segmentation labels are available. To assess performance in limited dataset scenarios, different setups with a decreasing amount of patients in the labeled dataset are investigated. We identify cascaded Self-Supervision as the best methodology, where we propose to employ Pseudo-Labeling and a self-supervised cascaded Student–Teacher model simultaneously. Our evaluation shows that in all scenarios, all investigated SSL methods outperform the respective low-data supervised baseline as well as state-of-the-art self-supervised approaches. This is most prominent in the very-low-labeled data regime, where for our proposed method we demonstrate

10.17 %

and

6.72 %

improvement in Dice Similarity Coefficient (DSC) for ACDC and MMWHS, respectively, compared with the low-data supervised approach, as well as

2.47 %

and

7.64 %

DSC improvement, respectively, when compared with related work. Moreover, in most experiments, our proposed method is able to greatly decrease the performance gap when compared to the fully supervised scenario, where all available labeled samples are used. We conclude that it is always beneficial to incorporate unlabeled data in cardiac MRI segmentation whenever it is present. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Medical Imaging Processing)

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19 pages, 2017 KiB

Open AccessArticle

Segmentation of Brain Tumors Using a Multi-Modal Segment Anything Model (MSAM) with Missing Modality Adaptation

by Jiezhen Xing and Jicong Zhang

Bioengineering 2025, 12(8), 871; https://doi.org/10.3390/bioengineering12080871 (registering DOI) - 12 Aug 2025

Abstract

This paper presents a novel multi-modal segment anything model (MSAM) for glioma tumor segmentation using structural MRI images and diffusion tensor imaging data. We designed an effective multimodal feature fusion block to effectively integrate features from different modalities of data, thereby improving the [...] Read more.

This paper presents a novel multi-modal segment anything model (MSAM) for glioma tumor segmentation using structural MRI images and diffusion tensor imaging data. We designed an effective multimodal feature fusion block to effectively integrate features from different modalities of data, thereby improving the accuracy of brain tumor segmentation. We have designed an effective missing modality training method to address the issue of missing modalities in actual clinical scenarios. To evaluate the effectiveness of MSAM, a series of experiments were conducted comparing its performance with U-Net across various modality combinations. The results demonstrate that MSAM consistently outperforms U-Net in terms of both Dice Similarity Coefficient and 95% Hausdorff Distance, particularly when structural modality data are used alone. Through feature visualization and the use of missing modality training, we show that MSAM can effectively adapt to missing data, providing robust segmentation even when key modalities are absent. Additionally, segmentation accuracy is influenced by tumor region size, with smaller regions presenting more challenges. These findings underscore the potential of MSAM in clinical applications where incomplete data or varying tumor sizes are prevalent. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based Medical Imaging Processing)

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20 pages, 2092 KiB

Open AccessReview

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Hepatocellular Carcinoma: A Review of Emerging Applications for Locoregional Therapy

by Xinyi M. Li, Tu Nguyen, Hiro D. Sparks, Kyunghyun Sung and Jason Chiang

Bioengineering 2025, 12(8), 870; https://doi.org/10.3390/bioengineering12080870 (registering DOI) - 12 Aug 2025

Abstract

Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for assessing tumor and parenchymal perfusion in the liver, playing a developing role in locoregional therapies (LRTs) for hepatocellular carcinoma (HCC). This review explores the conceptual underpinnings and early investigational [...] Read more.

Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for assessing tumor and parenchymal perfusion in the liver, playing a developing role in locoregional therapies (LRTs) for hepatocellular carcinoma (HCC). This review explores the conceptual underpinnings and early investigational stages of DCE-MRI for LRTs, including thermal ablation, transarterial chemoembolization (TACE), and transarterial radioembolization (TARE). Preclinical and early-phase studies suggest that DCE-MRI may offer valuable insights into HCC tumor microvasculature, treatment response, and therapy planning. In thermal ablation therapies, DCE-MRI provides a quantitative measurement of tumor microvasculature and perfusion, which can guide more effective energy delivery and estimation of ablation margins. For TACE, DCE-MRI parameters are proving their potential to describe treatment efficacy and predict recurrence, especially when combined with adjuvant therapies. In ⁹⁰Y TARE, DCE-MRI shows promise for refining dosimetry planning by mapping tumor blood flow to improve microsphere distribution. However, despite these promising applications, there remains a profound gap between early investigational studies and clinical translation. Current quantitative DCE-MRI research is largely confined to phantom models and initial feasibility assessments, with robust retrospective data notably lacking and prospective clinical trials yet to be initiated. With continued development, DCE-MRI has the potential to personalize LRT treatment approaches and serve as an important tool to enhance patient outcomes for HCC. Full article

(This article belongs to the Special Issue Recent Trends and Future Directions in Biosignal Processing and Applications: State-of-the-Art Reviews)

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17 pages, 4171 KiB

Open AccessArticle

Effects of Aging on Motor Unit Properties in Isometric Elbow Flexion

by Fang Qiu, Xiaodong Liu and Chen Chen

Bioengineering 2025, 12(8), 869; https://doi.org/10.3390/bioengineering12080869 (registering DOI) - 12 Aug 2025

Abstract

This study investigates age-related differences in motor unit (MU) properties and neuromuscular control during isometric elbow flexion across the human lifespan. High-density surface electromyography (sEMG) was recorded from the biceps brachii of 44 participants, divided into three groups: Child (8–14 years), Adult (20–40 [...] Read more.

This study investigates age-related differences in motor unit (MU) properties and neuromuscular control during isometric elbow flexion across the human lifespan. High-density surface electromyography (sEMG) was recorded from the biceps brachii of 44 participants, divided into three groups: Child (8–14 years), Adult (20–40 years), and Elder (65–80 years). MU spike trains were extracted noninvasively by sEMG decomposition. Then the discharge rate, MU action potential (MUAP) morphology, recruitment threshold, and common neural drive were quantified and compared across age groups. This study provides novel insights into force tracking performance, revealing that both children and elders exhibit higher errors compared to young adults, likely due to immature or declining motor control systems. Significant differences in MU discharge patterns were observed across force levels and age groups. Children and elders displayed lower MU discharge rates at low force levels, which increased at higher forces. In contrast, adults demonstrated higher MU action potential peak-to-peak amplitudes (PPV) and recruitment thresholds (RTs), along with steeper PPV-RT slopes, suggesting a narrower RT range in children and older adults. Principal component analysis revealed a strong correlation between common neural drive and force across all groups, with neural drive being weaker in elders. Overall, young adults exhibited the most efficient and synchronized MU control, while children and older adults showed distinct deviations in discharge intensity, recruitment strategies, and neural synergy. These findings comprehensively characterize MU adaptations across the lifespan, offering implications for developmental neurophysiology and age-specific neuromuscular diagnostics and interventions. Full article

(This article belongs to the Special Issue Musculoskeletal Function in Health and Disease)

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21 pages, 2629 KiB

Open AccessArticle

From Pixels to Precision—A Dual-Stream Deep Network for Pathological Nuclei Segmentation

by Rashid Nasimov, Kudratjon Zohirov, Adilbek Dauletov, Akmalbek Abdusalomov and Young Im Cho

Bioengineering 2025, 12(8), 868; https://doi.org/10.3390/bioengineering12080868 (registering DOI) - 12 Aug 2025

Abstract

Segmenting cell nuclei in histopathological images is an extremely important process for computational pathology, affecting not only the accuracy of a disease diagnosis but also the analysis of biomarkers and the assessment of cells performed on a large scale. Although many deep learning [...] Read more.

Segmenting cell nuclei in histopathological images is an extremely important process for computational pathology, affecting not only the accuracy of a disease diagnosis but also the analysis of biomarkers and the assessment of cells performed on a large scale. Although many deep learning models can take out global and local features, it is still difficult to find a good balance between semantic context and fine boundary precision, especially when nuclei are overlapping or have changed shapes. In this paper, we put forward a novel deep learning model named Dual-Stream HyperFusionNet (DS-HFN), which is capable of explicitly representing the global contextual and boundary-sensitive features for the robust nuclei segmentation task by first decoupling and then fusing them. The dual-stream encoder in DS-HFN can simultaneously acquire the semantic and edge-focused features, which can be later combined with the help of the attention-driven HyperFeature Embedding Module (HFEM). Additionally, the dual-decoder concept, together with the Gradient-Aligned Loss Function, facilitates structural precision by making the segmentation gradients that are predicted consistent with the ground-truth contours. On various benchmark datasets like TNBC and MoNuSeg, DS-HFN not only achieves better results than other 30 state-of-the-art models in all evaluation metrics but also is less computationally expensive. These findings indicate that DS-HFN provides a capability for accurate nuclei segmentation, which is essential for clinical diagnosis and biomarker analysis, across a wide range of tissues in digital pathology. Full article

(This article belongs to the Special Issue Medical Imaging Analysis: Current and Future Trends)

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13 pages, 8842 KiB

Open AccessArticle

Air-Assisted Dome Drainage in Acute Corneal Hydrops: A 3D-OCT-Guided Approach

by Antonio Moramarco, Matteo Elifani, Marian Sergiu Zimbru, Andrea Rosolia, Maurizio Mete and Luigi Fontana

Bioengineering 2025, 12(8), 867; https://doi.org/10.3390/bioengineering12080867 (registering DOI) - 12 Aug 2025

Abstract

To describe a technique for managing acute corneal hydrops in eyes with keratoconus using dome stromal drainage with intracameral air injection under real-time three-dimensional (3D) microscope-integrated optical coherence tomography (OCT) guidance. We describe a retrospective case series of six eyes from six patients [...] Read more.

To describe a technique for managing acute corneal hydrops in eyes with keratoconus using dome stromal drainage with intracameral air injection under real-time three-dimensional (3D) microscope-integrated optical coherence tomography (OCT) guidance. We describe a retrospective case series of six eyes from six patients with keratoconus who developed acute corneal hydrops. All eyes underwent intracameral air injection with controlled dome puncture for stromal fluid drainage, without the use of sutures. The procedure was performed using a 3D visualization system that enables integrated and simultaneous viewing of the surgical field and intraoperative OCT scan (a 3D digitally assisted visualization system that displayed a split-screen view of the surgical field and OCT cross-sections simultaneously). Postoperative resolution of edema and improvement in clarity were documented. The resolution of corneal edema allowed for subsequent mushroom-shaped penetrating keratoplasty performed with a femtosecond laser in four eyes of four patients. All six eyes showed significant resolution of corneal edema within 2 to 4 weeks. Stromal clefts collapsed rapidly after drainage. In each case, the thick edema was reduced to a confined leucoma. No intraoperative or postoperative complications were observed. All four eyes that underwent a femtosecond laser-assisted mushroom-shaped penetrating keratoplasty showed optimal anatomical and functional success. Air-assisted dome drainage, combined with simultaneous 3D and OCT visualization, is a safe and effective technique for treating acute corneal hydrops. This technology enables real-time decision-making and enhances surgical precision, opening the door to advanced procedures that are otherwise limited by corneal opacity. Full article

(This article belongs to the Special Issue Bioengineering Strategies for Ophthalmic Diseases)

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14 pages, 593 KiB

Open AccessReview

Advances in Label-Free Detection of Non-Muscle Invasive Bladder Cancer: A Critical Review

by Gabriela Vera, Javier Cerda-Infante, Mario I. Fernández, Miguel Sánchez-Encinas and Pablo A. Rojas

Bioengineering 2025, 12(8), 866; https://doi.org/10.3390/bioengineering12080866 (registering DOI) - 12 Aug 2025

Abstract

Non-muscle invasive bladder cancer (NMIBC) accounts for about 75% of new bladder cancer diagnoses. Early detection improves survival, yet routine white-light cystoscopy is invasive, costly, and can miss up to 45% of flat or small lesions. These shortcomings have prompted development of label-free [...] Read more.

Non-muscle invasive bladder cancer (NMIBC) accounts for about 75% of new bladder cancer diagnoses. Early detection improves survival, yet routine white-light cystoscopy is invasive, costly, and can miss up to 45% of flat or small lesions. These shortcomings have prompted development of label-free diagnostic tools that read the intrinsic optical, electrical, or mechanical signatures of urinary biomarkers without added labels. This review examines recent engineering advances in such platforms for NMIBC detection, focusing on analytical performance, readiness for clinical translation, and remaining barriers to adoption. We compare each technology with conventional cytology using key metrics such as limit of detection, diagnostic accuracy, analysis time, cohort size, and stage of clinical development. Surface-enhanced Raman spectroscopy and interferometric flow cytometry offer femtomolar sensitivity and more than 98% accuracy within minutes, while compact electrochemical sensors targeting NMP22, Galectin-1, and microRNAs reach sub-picogram levels on disposable chips. Standardized sample handling, multicenter validation, and robust cost-effectiveness data are now essential for these tools to advance point-of-care NMIBC surveillance. Full article

(This article belongs to the Special Issue Label-Free Cancer Detection)

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16 pages, 3631 KiB

Open AccessArticle

Controlled Mandibular Repositioning: A Novel Approach for Treatment of TMDs

by Diwakar Singh, Alain Landry, Martina Schmid-Schwap, Eva Piehslinger, André Gahleitner, Thomas Holzinger, Yilin Wang, Jiang Chen and Xiaohui Rausch-Fan

Bioengineering 2025, 12(8), 865; https://doi.org/10.3390/bioengineering12080865 - 11 Aug 2025

Abstract

Temporomandibular joint disorders (TMDs), particularly disc displacement with reduction (DDwR), are prevalent musculoskeletal conditions characterized by symptoms such as joint clicking, pain, and sometimes limited jaw movements. Accurate diagnosis requires a multidisciplinary approach, including clinical examination, imaging (MRI), and functional analysis. Among conservative [...] Read more.

Temporomandibular joint disorders (TMDs), particularly disc displacement with reduction (DDwR), are prevalent musculoskeletal conditions characterized by symptoms such as joint clicking, pain, and sometimes limited jaw movements. Accurate diagnosis requires a multidisciplinary approach, including clinical examination, imaging (MRI), and functional analysis. Among conservative treatment modalities, anterior repositioning splints (ARSs) are widely used to recapture the displaced discs and reposition the mandibular condyles. Determining the optimal therapeutic position (Th.P) for anterior repositioning splint fabrication remains challenging due to individual anatomical variability and a lack of standardized guidelines. This study introduces the controlled mandibular repositioning (CMR) method, which integrates clinical examination, imaging (MRI), computerized cephalometry, computerized condylography, neuromuscular palpation, and the Condylar Position Variator (CPV) to define an individualized Th.P. After treatment with CMR stabilizers (splints), the control MRI confirmed that in 36 out of 37 joints, the discs were repositioned to their normal position. There was a reduction in pain, as shown by VAS scores at the 6-month follow-up. This study demonstrated the effectiveness of the CMR method to find a precise therapeutic position, resulting in a 97.3% joint luxation reduction in DDwR. This study underscores the importance of precise, individualized Th.P determination for effective anterior repositioning. Full article

(This article belongs to the Special Issue New Sight for the Treatment of Dental Diseases: Updates and Direction)

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20 pages, 2068 KiB

Open AccessReview

Peripheral Nerve Regeneration Reimagined: Cutting-Edge Biomaterials and Biotechnological Innovations

by Ting Chak Lam, Zhenzhen Wu, Sang Jin Lee and Yiu Yan Leung

Bioengineering 2025, 12(8), 864; https://doi.org/10.3390/bioengineering12080864 - 11 Aug 2025

Abstract

Peripheral nerve injuries are frequent clinical issues that can lead to significant functional impairments, greatly impacting patients’ quality of life. Developing effective nerve regeneration methods is crucial for restoring function and ensuring the best possible outcomes. This review explores recent advances in nerve [...] Read more.

Peripheral nerve injuries are frequent clinical issues that can lead to significant functional impairments, greatly impacting patients’ quality of life. Developing effective nerve regeneration methods is crucial for restoring function and ensuring the best possible outcomes. This review explores recent advances in nerve regeneration, including nerve guidance conduits (NGCs), which are vital in bridging nerve gaps caused by injury and supporting repair. The field has seen significant progress in biomaterials and biotech, with biodegradable options like collagen and chitosan as well as non-biodegradable materials such as nylon. Innovations like 3D printing have allowed for more intricate conduit designs that more closely mimic natural nerves. Despite these progressions, research continues to focus on improving NGCs—often by adding cells or bioactive substances—to boost nerve regeneration and functional recovery. By analyzing current trends, this review aims to motivate clinicians and researchers to develop more comprehensive nerve repair strategies. It emphasizes approaches that combine scientific innovation with clinical practicality, fostering a more holistic and realistic outlook on enhancing patient outcomes in peripheral nerve regeneration. Full article

(This article belongs to the Special Issue Engineering Biodegradable-Implant Materials, 2nd Edition)

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17 pages, 5536 KiB

Open AccessArticle

Correlation Analysis of Suture Anchor Pull-Out Strength with Cortical Bone Thickness and Cancellous Bone Density on a Finite Element Model

by Jung Ho Kim, Jeon Jong Hyeok, Jae Hyun Woo and Sung Min Kim

Bioengineering 2025, 12(8), 863; https://doi.org/10.3390/bioengineering12080863 - 11 Aug 2025

Abstract

This study aimed to assess, using finite element analysis (FEA), the mechanical effects of cortical bone thickness and cancellous bone density on the pull-out strength of suture anchors. A PEEK anchor was modeled and embedded in synthetic bone blocks with cortical thicknesses ranging [...] Read more.

This study aimed to assess, using finite element analysis (FEA), the mechanical effects of cortical bone thickness and cancellous bone density on the pull-out strength of suture anchors. A PEEK anchor was modeled and embedded in synthetic bone blocks with cortical thicknesses ranging from 1 to 5 mm and cancellous densities of 10 PCF, 20 PCF, and 30 PCF. Axial tensile loading simulations were conducted for all combinations, and selected cases were validated through experimental pull-out tests using commercial synthetic bone, demonstrating agreement within ±6%. Both cortical thickness and cancellous density were found to enhance pull-out resistance, though the magnitude and pattern varied with density. At 10 PCF, pull-out strength increased linearly with cortical thickness. At 20 PCF, substantial gains were observed between 2 and 4 mm, followed by a plateau. At 30 PCF, most of the increase was confined between 2 and 3 mm, with minimal improvement thereafter. These findings suggest that fixation strategies should be adapted on the basis of bone quality and provide biomechanical insights to inform patient-specific implant design and surgical planning. Full article

(This article belongs to the Section Biomechanics and Sports Medicine)

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25 pages, 3340 KiB

Open AccessArticle

Approach to Standardized Material Characterization of the Human Lumbopelvic System: Testing and Evaluation

by Marc Gebhardt, Sascha Kurz, Fanny Grundmann, Thomas Klink, Volker Slowik, Christoph-Eckhard Heyde and Hanno Steinke

Bioengineering 2025, 12(8), 862; https://doi.org/10.3390/bioengineering12080862 (registering DOI) - 11 Aug 2025

Abstract

The osseo-ligamentous lumbopelvic complex is essential for musculoskeletal load transfer, yet location-specific material data and standardized test protocols remain scarce, which is a hindrance for comparability. Based on 91 specimen locations per cadaver (five cadavers, average age: 77.3 years), we developed detailed methods [...] Read more.

The osseo-ligamentous lumbopelvic complex is essential for musculoskeletal load transfer, yet location-specific material data and standardized test protocols remain scarce, which is a hindrance for comparability. Based on 91 specimen locations per cadaver (five cadavers, average age: 77.3 years), we developed detailed methods for specimen preparation and mechanical testing (bending, tensile, and compression) with defined boundary conditions. Multiple measurements were taken to assess repeatability. The proposed methods allow us to identify location-specific properties of the lumbopelvic system for the first time. Cortical bone exhibited an elastic modulus of 1750 MPa and an ultimate strength of 28.2 MPa, while those of trabecular bone were 32.7 MPa and 1.26 MPa, and soft tissues revealed values of 148 MPa and 14.3 MPa for fascial tissue and 103 MPa with 10.7 MPa for ligamentous tissue, respectively. The quantified properties for cortical and trabecular bone and soft tissues not only enhance the comparability of material properties but also support more accurate numerical simulations and implant design. Furthermore, the ease of implementation and standardization of these methods enable their widespread application, as well as the accumulation of a broad database and the setting of benchmarks for future investigations. Full article

(This article belongs to the Special Issue Biomechanics of Orthopaedic Rehabilitation)

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12 pages, 3255 KiB

Open AccessArticle

Plant-Derived Bone Substitute Presents Effective Osteointegration in Several Clinical Settings: A Pilot Study from a Single Center

by Gianluca Conza, Adriano Braile, Antonio Davide Vittoria, Nicola Di Cristofaro, Annalisa Itro, Gabriele Martin, Gabriella Toro, Pier Francesco Indelli, Vincenzo Salini and Giuseppe Toro

Bioengineering 2025, 12(8), 861; https://doi.org/10.3390/bioengineering12080861 - 11 Aug 2025

Abstract

Background: Bone loss management is a tough challenge in orthopedic and trauma surgery that is generally treated using graft or substitute. Bone is the second most common transplanted tissue behind blood. Autologous bone graft represents the gold standard, while allograft is generally used [...] Read more.

Background: Bone loss management is a tough challenge in orthopedic and trauma surgery that is generally treated using graft or substitute. Bone is the second most common transplanted tissue behind blood. Autologous bone graft represents the gold standard, while allograft is generally used as a secondary option, considering their impressive osteoconductive and osteoinductive properties. However, both allograft and autograft sources are limited. Therefore, synthetic bone substitutes gained popularity due to their low cost and ease of application. β-tri-Calcium phosphate (β-TCP) is a promising material implemented as a bone substitute. One of the limits of bone substitutes is related to their three-dimensional organization, which rarely replicates that of the normal bone. b.Bone™ is a novel bone substitute derived from rattan wood with a unique 3D structure that mimics the architecture of the human bone. This study aims to objectively evaluate the osteointegration of b.Bone™ in complex clinical settings. Methods: We retrospectively evaluated eight patients who underwent surgeries requiring filling bone loss through the use of b.Bone™. Osteointegration of the bone substitute was evaluated radiologically using a modified Van Hemert classification. Results: Eight patients were enrolled into this study: five females and three males with a mean age of 53,75 years old. b.Bone™ was applied in the following shapes: granules in four cases, cylinders in three cases and a prism in one. In four patients, the osteointegration reached a grade Van Hemert 4, three a grade 3, and only one a grade 2. Conclusions: β-TCP-based bone substitutes, such as those derived from rattan, appear to facilitate successful osteointegration in various clinical settings. Future studies with larger cohorts and longer follow-ups are necessary to evaluate the long-term efficacy of this promising substitute. Full article

(This article belongs to the Special Issue Biomaterials for Cartilage and Bone Tissue Engineering: Second Edition)

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27 pages, 2110 KiB

Open AccessReview

Curcumin-Loaded Drug Delivery Systems for Acute and Chronic Wound Management: A Review

by Xiaoxuan Deng, Jithendra Ratnayake and Azam Ali

Bioengineering 2025, 12(8), 860; https://doi.org/10.3390/bioengineering12080860 - 11 Aug 2025

Abstract

Wound healing is a physiological process including haemostasis, inflammation, proliferation, and remodelling. Acute wounds typically follow a predictable healing process, whereas chronic wounds cause prolonged inflammation and infection, failing to progress through typical healing phases and presenting significant clinical challenges. A combination of [...] Read more.

Wound healing is a physiological process including haemostasis, inflammation, proliferation, and remodelling. Acute wounds typically follow a predictable healing process, whereas chronic wounds cause prolonged inflammation and infection, failing to progress through typical healing phases and presenting significant clinical challenges. A combination of wound care techniques and therapeutic agents is required to manage chronic wounds effectively. Curcumin is a bioactive compound derived from Curcuma longa and has gained attention for its potent antioxidant, anti-inflammatory, and antibacterial properties. The first part of this review aims to provide a comprehensive overview of the physiology of wound healing, focusing on the pathophysiology and management of acute and chronic wounds, followed by the biological activity of curcumin in wound healing, emphasising its impact on promoting tissue repair. Finally, this review explores curcumin-loaded dressings, such as hydrogels, nanofibrous membranes, polymeric micelles, and films, offering controlled drug release and targeted curcumin delivery to enhance wound healing. Full article

(This article belongs to the Special Issue Advances and Innovations in Wound Repair and Regeneration)

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12 pages, 1646 KiB

Open AccessArticle

Mitomycin C in Ahmed Glaucoma Valve Implant Affects Surgical Outcomes

by Wei-Chun Lin, Sen Yang, Michelle R. Hribar and Aiyin Chen

Bioengineering 2025, 12(8), 859; https://doi.org/10.3390/bioengineering12080859 - 10 Aug 2025

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Abstract

Glaucoma is the leading cause of irreversible blindness worldwide, and the Ahmed Glaucoma Valve (AGV) implant is one of the most commonly performed surgeries to prevent glaucoma-related visual impairment. Mitomycin C is an anti-fibrotic agent that may prevent failure of AGV. This is [...] Read more.

Glaucoma is the leading cause of irreversible blindness worldwide, and the Ahmed Glaucoma Valve (AGV) implant is one of the most commonly performed surgeries to prevent glaucoma-related visual impairment. Mitomycin C is an anti-fibrotic agent that may prevent failure of AGV. This is a retrospective case–control study to evaluate surgical outcomes for patients undergoing AGV with adjunct mitomycin C (MMC) injections compared to those without MMC. Among the 142 eyes, 50 received adjunct MMC compared to 92 without MMC injections. IOPs at post-operative months 1, 3, and 6 were significantly lower in the MMC eyes (9.40, 12.01, 12.63 mmHg) compared to the No-MMC eyes (16.86, 15.87, 15.65 mmHg; p < 0.01). The number of post-operative glaucoma medications for the MMC group was lower at 1, 3, and 6 months (0.3, 0.4, 0.59) compared to the No-MMC group (0.7, 0.97, 1.05; p < 0.05). The difference in IOP and the number of medications was not statistically significant by 12 months. Adjunct MMC was associated with more transient hypotony but no long-term complications. These findings suggest that adjunct MMC improves short-term but not long-term surgical outcomes in AGV glaucoma implants. Full article

(This article belongs to the Special Issue Challenges for Managing Glaucoma in the 21st Century)

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16 pages, 1173 KiB

Open AccessReview

by Ezra T. Yoseph, Rukayat Taiwo, Ali Kiapour, Gavin Touponse, Elie Massaad, Marinos Theologitis, Janet Y. Wu, Theresa Williamson and Corinna C. Zygourakis

Bioengineering 2025, 12(8), 858; https://doi.org/10.3390/bioengineering12080858 - 10 Aug 2025

Viewed by 131

Abstract

Pregnancy induces substantial anatomical, hormonal, and biomechanical changes in the spine and pelvis to accommodate fetal growth and maintain postural adaptation. This narrative review synthesizes peer-reviewed evidence regarding pregnancy-related spinal biomechanics, with a particular focus on low back pain, spinopelvic alignment, sacroiliac joint [...] Read more.

Pregnancy induces substantial anatomical, hormonal, and biomechanical changes in the spine and pelvis to accommodate fetal growth and maintain postural adaptation. This narrative review synthesizes peer-reviewed evidence regarding pregnancy-related spinal biomechanics, with a particular focus on low back pain, spinopelvic alignment, sacroiliac joint dysfunction, and potential contributions to degenerative spinal conditions. A systematic search of PubMed, Embase, and Google Scholar was conducted using Boolean operators and relevant terms, yielding 1050 unique records, with 53 peer-reviewed articles ultimately cited. The review reveals that increased lumbar lordosis, ligamentous laxity, altered gait mechanics, and muscular deconditioning elevate mechanical load on the lumbar spine, predisposing up to 56% of pregnant individuals to low back pain. These changes are often associated with sacroiliac joint laxity, anterior pelvic tilt, and multiparity. Long-term risks may include degenerative disc disease and spondylolisthesis. Conservative interventions such as pelvic floor muscle training, prenatal exercise, and surface topography monitoring offer symptom relief and support early rehabilitation, although standardized protocols and longitudinal outcome data remain limited. Pregnancy-related spinal changes are multifactorial and clinically relevant; an interdisciplinary approach involving spinal biomechanics, physical therapy, and obstetric care is critical for optimizing maternal musculoskeletal health. Full article

(This article belongs to the Special Issue Spine Biomechanics)

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27 pages, 3312 KiB

Open AccessReview

Influence of Structure–Property Relationships of Polymeric Biomaterials for Engineering Multicellular Spheroids

by Sheetal Chowdhury and Amol V. Janorkar

Bioengineering 2025, 12(8), 857; https://doi.org/10.3390/bioengineering12080857 - 9 Aug 2025

Viewed by 196

Abstract

Two-dimensional cell culture systems lack the ability to replicate the complex, three-dimensional (3D) architecture and cellular microenvironments found in vivo. Multicellular spheroids (MCSs) present a promising alternative, with the ability to mimic native cell–cell and cell–matrix interactions and provide 3D architectures similar to [...] Read more.

Two-dimensional cell culture systems lack the ability to replicate the complex, three-dimensional (3D) architecture and cellular microenvironments found in vivo. Multicellular spheroids (MCSs) present a promising alternative, with the ability to mimic native cell–cell and cell–matrix interactions and provide 3D architectures similar to in vivo conditions. These factors are critical for various biomedical applications, including cancer research, tissue engineering, and drug discovery and development. Polymeric materials such as hydrogels, solid scaffolds, and ultra-low attachment surfaces serve as versatile platforms for 3D cell culture, offering tailored biochemical and mechanical cues to support cellular organization. This review article focuses on the structure–property relationships of polymeric biomaterials that influence MCS formation, growth, and functionality. Specifically, we highlight their physicochemical properties and their influence on spheroid formation using key natural polymers, including collagen, hyaluronic acid, chitosan, and synthetic polymers like poly(lactic-co-glycolic acid) and poly(N-isopropylacrylamide) as examples. Despite recent advances, several challenges persist, including spheroid loss during media changes, limited viability or function in long-term cultures, and difficulties in scaling for high-throughput applications. Importantly, the development of MCS platforms also supports the 3R principle (Replacement, Reduction, and Refinement) by offering ethical and physiologically relevant alternatives to animal testing. This review emphasizes the need for innovative biomaterials and methodologies to overcome these limitations, ultimately advancing the utility of MCSs in biomedical research. Full article

(This article belongs to the Special Issue 3D Cell Culture Systems: Current Technologies and Applications)

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13 pages, 2210 KiB

Open AccessArticle

The Use of Therapeutic Peptides in Combination with Full-Thickness Skin Columns to Improve Healing of Excisional Wounds

by Anders H. Carlsson, Ira M. Herman, Sean Christy, David Larson, Rodney K. Chan, Thomas N. Darling and Kristo Nuutila

Bioengineering 2025, 12(8), 856; https://doi.org/10.3390/bioengineering12080856 - 9 Aug 2025

Viewed by 174

Abstract

Split-thickness skin grafting (STSG) is the standard of care for skin replacement therapy. While STSG is a well-established technique, it has several limitations at both the donor and recipient sites. Full-thickness skin column (FTSC) grafting is an alternative approach that involves the orthogonal [...] Read more.

Split-thickness skin grafting (STSG) is the standard of care for skin replacement therapy. While STSG is a well-established technique, it has several limitations at both the donor and recipient sites. Full-thickness skin column (FTSC) grafting is an alternative approach that involves the orthogonal harvesting of small skin columns containing the epidermis, dermis, and associated skin appendages. Peptides have been shown to promote wound repair through various reparative and regenerative mechanisms. In this study, we aimed to evaluate the extent to which FTSCs and the matrix-derived peptide TSN6, individually or in combination, influenced the rate and quality of healing, as assessed by metrics such as epithelialization, epithelial thickness, and the presence of adnexal structures. TSN6 peptide and its scrambled form was synthetized in a laboratory and mixed with a carboxymethylcellulose (CMC) hydrogel. Up to 16 standardized full-thickness excisional wounds (∅ 5 cm) were created on the dorsum of two anesthetized pigs. FTSC biopsies (∅ 1.5 mm) were harvested from donor sites located on the rump of the pig at a ratio of up to eight 1.5 mm-diameter skin columns/1 cm². Subsequently, the wounds were randomized to receive either (1) FTSC + TSN6, (2) FTSC + scrambled peptide, (3) FTSC alone, and (4) blank CMC hydrogel. Healing was monitored for 14 or 28 days. After euthanasia, the wounds were excised and processed for histology. Additionally, non-invasive imaging systems were utilized to assess wound healing. By day 14, wounds treated with FTSC or FTSC + TSN6 were significantly more re-epithelialized compared to those treated with blank CMC hydrogel. By day 28, all FTSC-transplanted wounds were fully re-epithelialized. Notably, wounds treated with FTSC + TSN6 exhibited improved healing quality, characterized by a thicker neo-epidermis and increased rete ridges at day 28 post-transplantation. All FTSC-transplanted wounds healed better than the untransplanted controls. The TSN6 peptide further improved healing quality when applied in combination with FTSCs, particularly by enhancing epidermal maturation. Full article

(This article belongs to the Special Issue Advanced Techniques and Applications in Burn Reconstruction: A Bioengineering Perspective)

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34 pages, 9891 KiB

Open AccessArticle

The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound

by Blake VanBerlo, Jesse Hoey, Alexander Wong and Robert Arntfield

Bioengineering 2025, 12(8), 855; https://doi.org/10.3390/bioengineering12080855 - 8 Aug 2025

Viewed by 141

Abstract

Data augmentation is a central component of joint embedding self-supervised learning (SSL). Approaches that work for natural images may not always be effective in medical imaging tasks. This study systematically investigated the impact of data augmentation and preprocessing strategies in SSL for lung [...] Read more.

Data augmentation is a central component of joint embedding self-supervised learning (SSL). Approaches that work for natural images may not always be effective in medical imaging tasks. This study systematically investigated the impact of data augmentation and preprocessing strategies in SSL for lung ultrasound. Three data augmentation pipelines were assessed: (1) a baseline pipeline commonly used across imaging domains, (2) a novel semantic-preserving pipeline designed for ultrasound, and (3) a distilled set of the most effective transformations from both pipelines. Pretrained models were evaluated on multiple classification tasks: B-line detection, pleural effusion detection, and COVID-19 classification. Experiments revealed that semantics-preserving data augmentation resulted in the greatest performance for COVID-19 classification—a diagnostic task requiring global image context. Cropping-based methods yielded the greatest performance on the B-line and pleural effusion object classification tasks, which require strong local pattern recognition. Lastly, semantics-preserving ultrasound image preprocessing resulted in increased downstream performance for multiple tasks. Guidance regarding data augmentation and preprocessing strategies was synthesized for developers working with SSL in ultrasound. Full article

(This article belongs to the Special Issue Mathematical Models for Medical Diagnosis and Testing)

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21 pages, 1746 KiB

Open AccessArticle

Automated Mucormycosis Diagnosis from Paranasal CT Using ResNet50 and ConvNeXt Small

by Serdar Ferit Toprak, Serkan Dedeoğlu, Günay Kozan, Muhammed Ayral, Şermin Can, Ömer Türk and Mehmet Akdağ

Bioengineering 2025, 12(8), 854; https://doi.org/10.3390/bioengineering12080854 - 8 Aug 2025

Viewed by 243

Abstract

Purpose: Mucormycosis is a life-threatening fungal infection, where rapid diagnosis is critical. We developed a deep learning approach using paranasal computed tomography (CT) images to test whether mucormycosis can be detected automatically, potentially aiding or expediting the diagnostic process that traditionally relies on [...] Read more.

Purpose: Mucormycosis is a life-threatening fungal infection, where rapid diagnosis is critical. We developed a deep learning approach using paranasal computed tomography (CT) images to test whether mucormycosis can be detected automatically, potentially aiding or expediting the diagnostic process that traditionally relies on biopsy. Methods: In this retrospective study, 794 CT images (from patients with mucormycosis, nasal polyps, or normal findings) were analyzed. Images were resized and augmented for training. Two transfer learning models (ResNet50 and ConvNeXt Small) were fine-tuned to classify images into the three categories. We employed a 70/30 train-test split (with five-fold cross-validation) and evaluated performance using accuracy, precision, recall, F1-score, and confusion matrices. Results: The ConvNeXt Small model achieved 100% accuracy on the test set (precision/recall/F1-score = 1.00 for all classes), while ResNet50 achieved 99.16% accuracy (precision ≈0.99, recall ≈0.99). Cross-validation yielded consistent results (ConvNeXt accuracy ~99% across folds), indicating no overfitting. An ablation study confirmed the benefit of transfer learning, as training ConvNeXt from scratch led to lower accuracy (~85%) Conclusions: Our findings demonstrate that deep learning models can accurately and non-invasively detect mucormycosis from CT scans, potentially flagging suspected cases for prompt treatment. These models could serve as rapid screening tools to complement standard diagnostic methods (histopathology), although we emphasize that they are adjuncts and not replacements for biopsy. Future work should validate these models on external datasets and investigate their integration into clinical workflows for earlier intervention in mucormycosis. Full article

(This article belongs to the Special Issue Machine Learning-Aided Medical Image Analysis)

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17 pages, 1328 KiB

Open AccessArticle

Developing a Classification of Spinal Medical Devices: Has the Time Come? Review of the Literature and a Proposal for Spine Registries

by Veronica Mari, Simona Pascucci, Andrea Piazzolla, Pedro Berjano, Michela Franzò, Letizia Sampaolo, Eugenio Carrani and Marina Torre

Bioengineering 2025, 12(8), 853; https://doi.org/10.3390/bioengineering12080853 (registering DOI) - 8 Aug 2025

Viewed by 110

Abstract

Registries require standardized component libraries based on predefined taxonomies to ensure detailed and structured descriptions of implanted devices, enabling effective monitoring of implant safety. Considering the growing use of spinal implantable devices, we aimed to propose a comprehensive classification framework for spinal devices, [...] Read more.

Registries require standardized component libraries based on predefined taxonomies to ensure detailed and structured descriptions of implanted devices, enabling effective monitoring of implant safety. Considering the growing use of spinal implantable devices, we aimed to propose a comprehensive classification framework for spinal devices, to be integrated into the Italian Spine registry framework. The taxonomy was created using a detailed process that included reviewing existing literature, analyzing technical documents, selecting important device characteristics, obtaining feedback from manufacturers, and converting the information into a format suitable for IT systems. Our findings showed the lack of a globally accepted classification system. We identified four primary categories, further refined into subcategories, complemented by attributes for device identification, traceability, and characterization, then structured them using XSD schemas. Our proposal represents the first known attempt to implement a taxonomy for spinal implants, with the potential to serve as an international reference. A structured classification system would enhance registry interoperability, facilitate cross-registry comparability, and improve the early detection of adverse events, thereby strengthening patient safety and clinical outcomes. Furthermore, the adoption of a unified classification framework would improve surgeons’ clinical practice and support policymakers in developing early prevention strategies, ultimately improving patient care. Full article

(This article belongs to the Special Issue Medical Devices and Implants, 2nd Edition)

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19 pages, 2304 KiB

Open AccessArticle

Integrating AI with Advanced Hyperspectral Imaging for Enhanced Classification of Selected Gastrointestinal Diseases

by Chu-Kuang Chou, Kun-Hua Lee, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Ashok Kumar, Danat Gutema, Po-Chun Yang, Chien-Wei Huang and Hsiang-Chen Wang

Bioengineering 2025, 12(8), 852; https://doi.org/10.3390/bioengineering12080852 - 8 Aug 2025

Viewed by 203

Abstract

Ulcerative colitis, polyps, esophagitis, and other gastrointestinal (GI) diseases significantly impact health, making early detection crucial for reducing mortality rates and improving patient outcomes. Traditional white light imaging (WLI) is commonly used during endoscopy to identify abnormalities in the gastrointestinal tract. However, insufficient [...] Read more.

Ulcerative colitis, polyps, esophagitis, and other gastrointestinal (GI) diseases significantly impact health, making early detection crucial for reducing mortality rates and improving patient outcomes. Traditional white light imaging (WLI) is commonly used during endoscopy to identify abnormalities in the gastrointestinal tract. However, insufficient contrast often limits its effectiveness, making it challenging to distinguish between healthy and unhealthy tissues, particularly when identifying subtle mucosal and vascular abnormalities. These limitations have prompted the need for more advanced imaging techniques that enhance pathological visualization and facilitate early diagnosis. Therefore, this study investigates the integration of the Spectrum-Aided Vision Enhancer (SAVE) mechanism to improve WLI images and increase disease detection accuracy. This approach transforms standard WLI images into hyperspectral imaging (HSI) representations, creating narrow-band imaging (NBI-like) visuals with enhanced contrast and tissue differentiation, thereby improving the visualization of vascular and mucosal structures critical for diagnosing GI disorders. This transformation allows for a clearer representation of blood vessels and membrane formations, which is essential for determining the presence of GI diseases. The dataset for this study comprises WLI images alongside SAVE-enhanced images, including four categories: ulcerative colitis, polyps, esophagitis, and healthy GI tissue. These images are organized into training, validation, and test sets to develop a deep learning-based classification model. Utilizing principal component analysis (PCA) and multiple regression analysis for spectral standardization ensures that the improved images retain spectral characteristics that are vital for clinical applications. By merging deep learning techniques with advanced imaging enhancements, this study aims to create an artificial intelligence (AI)–driven diagnostic system capable of early and accurate detection of GI diseases. InceptionV3 attained an overall accuracy of 94% in both scenarios; SAVE produced a modest enhancement in the ulcerative colitis F1-score from 92% to 93%, while the F1-scores for other classes exceeded 96%. SAVE resulted in a 10% increase in YOLOv8x accuracy, reaching 89%, with ulcerative colitis F1 improving to 82% and polyp F1 rising to 76%. VGG16 enhanced accuracy from 85% to 91%, and the F1-score for polyps improved from 68% to 81%. These findings confirm that SAVE enhancement consistently improves disease classification across diverse architectures, offers a practical, hardware-independent approach to hyperspectral-quality images, and enhances the accuracy of gastrointestinal screening. Furthermore, this research seeks to provide a practical and effective solution for clinical applications, improving diagnostic accuracy and facilitating superior patient care. Full article

(This article belongs to the Special Issue Advancing Healthcare: Harnessing AI for Diagnostic and Therapeutic Innovations)

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20 pages, 3517 KiB

Open AccessReview

Review of Cardiovascular Mock Circulatory Loop Designs and Applications

by Victor K. Tsui and Daniel Ewert

Bioengineering 2025, 12(8), 851; https://doi.org/10.3390/bioengineering12080851 - 7 Aug 2025

Viewed by 194

Abstract

Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological [...] Read more.

Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological and pathological conditions. While many studies focus on custom MCL designs tailored to specific applications, few have systematically reviewed their use in device testing, and none have assessed their broader utility across diverse biomedical domains. This comprehensive review categorizes MCL designs into three types: mechanical, computational, and hybrid. Applications are classified into four major areas: Cardiovascular Devices Testing, Clinical Training and Education, Hemodynamics and Blood Flow Studies, and Disease Modeling. Most existing MCLs are complex, highly specialized, and difficult to reproduce, highlighting the need for simplified, standardized, and programmable hybrid systems. Improved validation and waveform fidelity—particularly through incorporation of the dicrotic notch and other waveform parameters—are critical for advancing MCL reliability. Furthermore, integration of machine learning and artificial intelligence holds significant promise for enhancing waveform analysis, diagnostics, predictive modeling, and personalized care. In conclusion, the development of MCLs should prioritize standardization, simplification, and broader accessibility to expand their impact across biomedical research and clinical translation. Full article

(This article belongs to the Special Issue Cardiovascular Models and Biomechanics)

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17 pages, 2763 KiB

Open AccessArticle

Extended Reality-Based Proof-of-Concept for Clinical Assessment Balance and Postural Disorders for Personalized Innovative Protocol

by Fabiano Bini, Michela Franzò, Alessia Finti, Francesca Tiberi, Veronica Maria Teresa Grillo, Edoardo Covelli, Maurizio Barbara and Franco Marinozzi

Bioengineering 2025, 12(8), 850; https://doi.org/10.3390/bioengineering12080850 - 7 Aug 2025

Viewed by 234

Abstract

Background: Clinical assessment of balance and postural disorders is usually carried out through several common practices including tests such as the Subjective Visual Vertical (SVV) and Limit of Stability (LOS). Nowadays, several cutting-edge technologies have been proposed as supporting tools for stability evaluation. [...] Read more.

Background: Clinical assessment of balance and postural disorders is usually carried out through several common practices including tests such as the Subjective Visual Vertical (SVV) and Limit of Stability (LOS). Nowadays, several cutting-edge technologies have been proposed as supporting tools for stability evaluation. Extended Reality (XR) emerges as a powerful instrument. This proof-of-concept study aims to assess the feasibility and potential clinical utility of a novel MR-based framework integrating HoloLens 2, Wii Balance Board, and Azure Kinect for multimodal balance assessment. An innovative test is also introduced, the Innovative Dynamic Balance Assessment (IDBA), alongside an MR version of the SVV test and the evaluation of their performance in a cohort of healthy individuals. Results: All participants reported SVV deviations within the clinically accepted ±2° range. The IDBA results revealed consistent sway and angular profiles across participants, with statistically significant differences in posture control between opposing target directions. System outputs were consistent, with integrated parameters offering a comprehensive representation of postural strategies. Conclusions: The MR-based framework successfully delivers integrated, multimodal measurements of postural control in healthy individuals. These findings support its potential use in future clinical applications for balance disorder assessment and personalized rehabilitation. Full article

(This article belongs to the Section Biomedical Engineering and Biomaterials)

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45 pages, 4319 KiB

Open AccessReview

Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma

by Georgios Lekkas, Eleni Vrochidou and George A. Papakostas

Bioengineering 2025, 12(8), 849; https://doi.org/10.3390/bioengineering12080849 - 6 Aug 2025

Viewed by 441

Abstract

The advancement of artificial intelligence (AI), deep learning, and radiomics has introduced novel methodologies for the detection, classification, prognosis, and treatment evaluation of pancreatic ductal adenocarcinoma (PDAC). As the integration of AI into medical imaging continues to evolve, its potential to enhance early [...] Read more.

The advancement of artificial intelligence (AI), deep learning, and radiomics has introduced novel methodologies for the detection, classification, prognosis, and treatment evaluation of pancreatic ductal adenocarcinoma (PDAC). As the integration of AI into medical imaging continues to evolve, its potential to enhance early detection, refine diagnostic precision, and optimize treatment strategies becomes increasingly evident. However, despite significant progress, various challenges remain, particularly in terms of clinical applicability, generalizability, interpretability, and integration into routine practice. Understanding the current state of research is crucial for identifying gaps in the literature and exploring opportunities for future advancements. This literature review aims to provide a comprehensive overview of the existing studies on AI applications in PDAC, with a focus on disease detection, classification, survival prediction, treatment response assessment, and radiogenomics. By analyzing the methodologies, findings, and limitations of these studies, we aim to highlight the strengths of AI-driven approaches while addressing critical gaps that hinder their clinical translation. Furthermore, this review aims to discuss future directions in the field, emphasizing the need for multi-institutional collaborations, explainable AI models, and the integration of multi-modal data to advance the role of AI in personalized medicine for PDAC. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Explainable Artificial Intelligence in Biomedical Data Mining, and Disease Diagnosis Frameworks)

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17 pages, 1800 KiB

Open AccessArticle

Healing Kinetics of Sinus Lift Augmentation Using Biphasic Calcium Phosphate Granules: A Case Series in Humans

by Michele Furlani, Valentina Notarstefano, Nicole Riberti, Emira D’Amico, Tania Vanessa Pierfelice, Carlo Mangano, Elisabetta Giorgini, Giovanna Iezzi and Alessandra Giuliani

Bioengineering 2025, 12(8), 848; https://doi.org/10.3390/bioengineering12080848 - 6 Aug 2025

Viewed by 285

Abstract

Sinus augmentation provides a well-established model for investigating the three-dimensional morphometry and macromolecular dynamics of bone regeneration, particularly when using biphasic calcium phosphate (BCP) graft substitutes. This case series included six biopsies from patients who underwent maxillary sinus augmentation using BCP granules composed [...] Read more.

Sinus augmentation provides a well-established model for investigating the three-dimensional morphometry and macromolecular dynamics of bone regeneration, particularly when using biphasic calcium phosphate (BCP) graft substitutes. This case series included six biopsies from patients who underwent maxillary sinus augmentation using BCP granules composed of 30% hydroxyapatite (HA) and 70% β-tricalcium phosphate (β-TCP). Bone core biopsies were obtained at healing times of 6 months, 9 months, and 12 months. Histological evaluation yielded qualitative and quantitative insights into new bone distribution, while micro-computed tomography (micro-CT) and Raman microspectroscopy (RMS) were employed to assess the three-dimensional architecture and macromolecular composition of the regenerated bone. Micro-CT analysis revealed progressive maturation of the regenerated bone microstructure over time. At 6 months, the apical regenerated area exhibited a significantly higher mineralized volume fraction (58 ± 5%) compared to the basal native bone (44 ± 11%; p = 0.0170), as well as significantly reduced trabecular spacing (Tb.Sp: 187 ± 70 µm vs. 325 ± 96 µm; p = 0.0155) and degree of anisotropy (DA: 0.37 ± 0.05 vs. 0.73 ± 0.03; p < 0.0001). By 12 months, the mineralized volume fraction in the regenerated area (53 ± 5%) was statistically comparable to basal bone (44 ± 3%; p > 0.05), while Tb.Sp (211 ± 20 µm) and DA (0.23 ± 0.09) remained significantly lower (Tb.Sp: 395 ± 41 µm, p = 0.0041; DA: 0.46 ± 0.04, p = 0.0001), indicating continued structural remodelling and organization. Raman microspectroscopy further revealed dynamic macromolecular changes during healing. Characteristic β-TCP peaks (e.g., 1315, 1380, 1483 cm⁻¹) progressively diminished over time and were completely absent in the regenerated tissue at 12 months, contrasting with their partial presence at 6 months. Simultaneously, increased intensity of collagen-specific bands (e.g., Amide I at 1661 cm⁻¹, Amide III at 1250 cm⁻¹) and carbonate peaks (1065 cm⁻¹) reflected active matrix formation and mineralization. Overall, this case series provides qualitative and quantitative evidence that bone regeneration and integration of BCP granules in sinus augmentation continues beyond 6 months, with ongoing maturation observed up to 12 months post-grafting. Full article

(This article belongs to the Special Issue Advanced Physical Techniques in Tissue Engineering and Regenerative Medicine)

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25 pages, 4450 KiB

Open AccessArticle

Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images

by Asieh Soltanipour, Roya Arian, Ali Aghababaei, Fereshteh Ashtari, Yukun Zhou, Pearse A. Keane and Raheleh Kafieh

Bioengineering 2025, 12(8), 847; https://doi.org/10.3390/bioengineering12080847 (registering DOI) - 6 Aug 2025

Viewed by 319

Abstract

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to [...] Read more.

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)

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19 pages, 1185 KiB

Open AccessArticle

PredictMed-CDSS: Artificial Intelligence-Based Decision Support System Predicting the Probability to Develop Neuromuscular Hip Dysplasia

by Carlo M. Bertoncelli, Federico Solla, Michal Latalski, Sikha Bagui, Subhash C. Bagui, Stefania Costantini and Domenico Bertoncelli

Bioengineering 2025, 12(8), 846; https://doi.org/10.3390/bioengineering12080846 - 6 Aug 2025

Viewed by 285

Abstract

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability [...] Read more.

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery (p = 0.001), poor motor function (p = 0.004), truncal tone disorder (p = 0.008), scoliosis (p = 0.031), number of affected limbs (p = 0.05), and epilepsy (p = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients. Full article

(This article belongs to the Special Issue New Insights into Machine Learning and Biomedicine: Updates and Directions)

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21 pages, 365 KiB

Open AccessArticle

The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson’s Disease Detection

by Jonathan Starcke, James Spadafora, Jonathan Spadafora, Phillip Spadafora and Milan Toma

Bioengineering 2025, 12(8), 845; https://doi.org/10.3390/bioengineering12080845 - 6 Aug 2025

Viewed by 328

Abstract

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and [...] Read more.

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and jeopardizing future advances in patient care. For instance, machine learning models have shown high accuracy in diagnosing Parkinson’s Disease when trained on clinical features that are themselves diagnostic, such as tremor and rigidity. This study systematically investigates the impact of data leakage and feature selection on the true clinical utility of machine learning models for early Parkinson’s Disease detection. We constructed two experimental pipelines: one excluding all overt motor symptoms to simulate a subclinical scenario and a control including these features. Nine machine learning algorithms were evaluated using a robust three-way data split and comprehensive metric analysis. Results reveal that, without overt features, all models exhibited superficially acceptable F1 scores but failed catastrophically in specificity, misclassifying most healthy controls as Parkinson’s Disease. The inclusion of overt features dramatically improved performance, confirming that high accuracy was due to data leakage rather than genuine predictive power. These findings underscore the necessity of rigorous experimental design, transparent reporting, and critical evaluation of machine learning models in clinically realistic settings. Our work highlights the risks of overestimating model utility due to data leakage and provides guidance for developing robust, clinically meaningful machine learning tools for early disease detection. Full article

(This article belongs to the Special Issue Mathematical Models for Medical Diagnosis and Testing)

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