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Diagnostics
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Artificial Intelligence in Cardiovascular and Stroke Imaging
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Artificial Intelligence in Cardiovascular and Stroke Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 6045

Editor

Dr. Jasjit S. Suri

E-Mail Website
Guest Editor
Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence (AI) are transforming cardiovascular and stroke imaging, enabling faster, more accurate diagnoses and personalized treatment strategies. This Special Issue aims to explore cutting-edge AI applications in cardiac and neurovascular imaging, including deep learning for automated lesion detection, predictive modeling for disease progression, and AI-enhanced image reconstruction. We invite original research, reviews, and case studies that highlight innovations in machine learning, computer vision, and big data analytics as applied to echocardiography, CT, MRI, and other imaging modalities. Topics of interest include AI-driven risk stratification, real-time decision support systems, explainable AI in clinical practice, and ethical considerations in AI deployment. By bringing together interdisciplinary research, this Special Issue seeks to advance the integration of AI into cardiovascular and stroke care, improving patient outcomes and healthcare efficiency.

We welcome contributions from researchers, clinicians, and engineers working at the intersection of AI and medical imaging. Submit your manuscript to be part of this pivotal discussion on the future of intelligent diagnostics and therapy.

Dr. Jasjit S. Suri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • cardiovascular
  • stroke imaging
  • diagnosis
  • prognosis

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Published Papers (5 papers)

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Research

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18 pages, 3364 KB  
Article
Machine Learning-Driven Probability of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement
by Marcel Abras, Daniela Bursacovschi, Ecaterina Pasat, Maria-Magdalena Vicol, Tatiana Abras, Lucia Mazur-Nicorici and Oleg Arnaut
Diagnostics 2026, 16(11), 1720; https://doi.org/10.3390/diagnostics16111720 - 3 Jun 2026
Viewed by 523
Abstract
Background/Objectives: Permanent pacemaker implantation (PPI) remains one of the most common complications following transcatheter aortic valve replacement (TAVR). Identifying patients at increased risk for post-procedural conduction disturbances is clinically important for procedural planning and patient management. The aim of this study was to [...] Read more.
Background/Objectives: Permanent pacemaker implantation (PPI) remains one of the most common complications following transcatheter aortic valve replacement (TAVR). Identifying patients at increased risk for post-procedural conduction disturbances is clinically important for procedural planning and patient management. The aim of this study was to develop and evaluate a machine learning-based model for predicting the risk of PPI after TAVR. Methods: This prospective study was conducted between 2019 and 2025, and included 179 patients with severe aortic stenosis who underwent TAVR. Patient eligibility was determined by a multidisciplinary Heart Team based on clinical, echocardiographic, and imaging criteria. The primary endpoint was PPI occurring during hospitalization or within 30 days after the procedure. Statistical analyses were performed using RStudio (v. 2024.09.1+394)and Python (v.3.12.3), including comparative tests for continuous and categorical variables, receiver operating characteristic analysis to assess model performance, and SHapley Additive exPlanations (SHAP) to evaluate feature importance and model interpretability. Results: A total of 179 patients undergoing TAVR were included in the analysis. PPI occurred in 17 patients (9.5%) within 30 days after the procedure. A machine learning model was developed to predict post-TAVR PPI. The model demonstrated good predictive performance, with an overall accuracy of 0.944 and a weighted F1-score of 0.947. The confusion matrix showed that the model correctly classified 155 patients without PPI and 14 patients with PPI, with only a small number of false predictions. Explainability analyses using SHAP and permutation feature importance revealed that anatomical and procedural variables had the greatest impact on model predictions. The most influential predictors included valve size, right coronary sinus diameter, prosthetic valve diameter, and mean aortic annulus diameter. In contrast, baseline clinical variables such as left ventricular ejection fraction, previous myocardial infarction, and mean transaortic gradient showed a comparatively lower contribution to the prediction of PPI after TAVR. Conclusions: This study demonstrates that machine learning models can effectively predict the risk of PPI after TAVR. Anatomical characteristics of the aortic root and prosthesis-related parameters were the main determinants of PPI, whereas baseline clinical variables had a lower impact. The use of explainable artificial intelligence methods, such as SHAP analysis, may improve risk stratification and support procedural planning in patients undergoing TAVR. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular and Stroke Imaging)
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31 pages, 11286 KB  
Article
ABR-UNet3D: Aspect-Aware Boundary-Resilient Attention for Robust Cardiac MRI Segmentation
by Serdar Akyel, Zeki Cetinkaya, Fatih Topaloglu and Eser Sert
Diagnostics 2026, 16(11), 1598; https://doi.org/10.3390/diagnostics16111598 - 23 May 2026
Viewed by 330
Abstract
Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and [...] Read more.
Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular and Stroke Imaging)
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17 pages, 4375 KB  
Article
Improving the Detection Performance of Cardiovascular Diseases from Heart Sound Signals with a New Deep Learning-Based Approach
by Ozgen Safak, Mehmet Tolga Hekim, Tolga Cakmak, Fatih Demir and Kursat Demir
Diagnostics 2025, 15(18), 2379; https://doi.org/10.3390/diagnostics15182379 - 18 Sep 2025
Cited by 1 | Viewed by 1307
Abstract
Background/Objectives: Cardiovascular diseases are among the leading causes of death worldwide. Early diagnosis of these conditions minimizes the risk of future death. Listening to heart sounds with a stethoscope is one of the easiest and fastest methods for diagnosing heart conditions. While [...] Read more.
Background/Objectives: Cardiovascular diseases are among the leading causes of death worldwide. Early diagnosis of these conditions minimizes the risk of future death. Listening to heart sounds with a stethoscope is one of the easiest and fastest methods for diagnosing heart conditions. While heart sounds are a quick and easy diagnostic method, they require significant expert interpretation. Recently, artificial intelligence models trained based on these expert interpretations have become popular in the development of decision support systems. Methods: The proposed approach uses the popular 2016 PhysioNet/CinC Challenge dataset for PCG signals. Spectrogram image transformation was then performed to increase the representativeness of these signals. A deep learning-based model that allows for the simultaneous training of residual and attention blocks and the MLP-mixer model was used for feature extraction. A new algorithm combining the strengths of NCA and ReliefF algorithms was proposed to select the strongest features in the feature set. The SVM algorithm was used for classification. Results: With this proposed approach, over 98% success was achieved in all accuracy, sensitivity, specificity, precision, and F1-score metrics. Conclusions: As a result, an artificial intelligence-based decision support system that detects cardiovascular diseases with high accuracy is presented. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular and Stroke Imaging)
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Review

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36 pages, 3868 KB  
Review
Women’s Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review
by Ekta Tiwari, Dipti Shrimankar, Mahesh Maindarkar, Luca Saba and Jasjit S. Suri
Diagnostics 2026, 16(8), 1158; https://doi.org/10.3390/diagnostics16081158 - 14 Apr 2026
Viewed by 833
Abstract
Background: Women face underdiagnosed cardiovascular disease (CVD)/stroke risks due to sex-specific pathophysiological mechanisms, including hormonal variations such as oestrogen decline, adverse pregnancy outcomes (APOs), endothelial dysfunction, autoimmune-mediated factors, and sexual dimorphism in cardiac remodelling. Conventional risk assessment tools, predominantly calibrated to male [...] Read more.
Background: Women face underdiagnosed cardiovascular disease (CVD)/stroke risks due to sex-specific pathophysiological mechanisms, including hormonal variations such as oestrogen decline, adverse pregnancy outcomes (APOs), endothelial dysfunction, autoimmune-mediated factors, and sexual dimorphism in cardiac remodelling. Conventional risk assessment tools, predominantly calibrated to male pathophysiology, lack sensitivity in detecting these female-specific determinants. We hypothesise that artificial intelligence (AI), machine learning (ML) and deep learning (DL) may offer a transformative approach by integrating multimodal data, including pathological biomarkers, clinical history, and vascular imaging, to enable precision CVD/stroke risk stratification, pending rigorous external validation in sex-stratified cohorts. Method: This narrative review adopts a PRISMA-informed study selection framework and oversees gender-specific biomarkers, including vasoactive peptides (adrenomedullin), adipocytokines (adiponectin), inflammatory mediators (hs-CRP, IL-6), and thrombogenic factors (homocysteine, D-dimer), alongside clinical variables (APOs, autoimmune disorders) and ultrasonographic markers, carotid intima-media thickness (cIMT), plaque burden and plaque area (PA). Advanced ML/DL algorithms were employed to synthesise these heterogeneous datasets, identifying nonlinear interactions for better outcomes. Findings: Key insights reveal that hormonal dynamics (e.g., hypoestrogenism post-menopause) modulate CVD risk, while APOs induce persistent endothelial dysfunction and subclinical atherosclerosis. Biomarker sexual dimorphism is evident; hs-CRP exhibits higher baseline levels in women, whereas adiponectin declines with metabolic dysfunction. Radiomic features (cIMT progression, plaque morphology) are a well-established biomarker for CVD risk stratification. Conclusions: The integration of AI-driven multimodal systems holds the potential to enable a paradigm shift from population-based to personalised risk assessment, addressing critical gaps in female CVD health. However, this potential is currently at the early validation stage, and widespread clinical implementation requires prospective, externally validated, and ethnically diverse studies. Future applications should incorporate longitudinal biomarker profiling and advanced imaging, namely shear wave elastography and plaque radiomics, to optimise predictive models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular and Stroke Imaging)
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36 pages, 955 KB  
Review
Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity—A Comprehensive Review
by Miruna Florina Ștefan, Lucia Ștefania Magda and Dragoș Vinereanu
Diagnostics 2026, 16(3), 488; https://doi.org/10.3390/diagnostics16030488 - 5 Feb 2026
Cited by 3 | Viewed by 2228
Abstract
Background: Cardiotoxicity is a major limitation of chemotherapy and radiotherapy for thoracic and systemic cancers, contributing significantly to morbidity and mortality among survivors. Early prediction and prevention are critical to balance oncologic efficacy with cardiovascular safety. Artificial intelligence (AI) offers powerful tools to [...] Read more.
Background: Cardiotoxicity is a major limitation of chemotherapy and radiotherapy for thoracic and systemic cancers, contributing significantly to morbidity and mortality among survivors. Early prediction and prevention are critical to balance oncologic efficacy with cardiovascular safety. Artificial intelligence (AI) offers powerful tools to improve risk stratification, enable earlier detection of subclinical injury, and guide treatment planning in cardio-oncology. Methods: We performed a comprehensive review of the literature on AI applications for cancer therapy-related cardiotoxicity. Evidence was identified from PubMed, Scopus, and Web of Science, focusing on electrocardiography, biomarkers, proteomics, extracellular vesicles, genomics, advanced imaging (echocardiography, cardiac magnetic resonance, computed tomography, nuclear imaging), and radiotherapy dose modeling (dosiomics). Translational insights from animal models and in vitro systems were also included. Methodological quality was appraised with reference to TRIPOD-AI, PROBAST-AI, and CLAIM standards. Results: AI applications span multiple domains. Machine learning models integrating biomarkers, exosomes, and extracellular vesicles show promise for noninvasive early detection. Deep learning enables automated analysis of echocardiographic strain and cardiac MRI mapping, while radiomics and dosiomics approaches combine imaging with cardiac substructure dose maps to predict and prevent late radiation-induced injury. Preclinical studies demonstrate AI-driven advances in small-animal imaging, histopathology quantification, and multi-omics data integration, supporting the discovery of translational biomarkers. Despite encouraging performance, most models remain limited by small cohorts, methodological heterogeneity, and scarce external validation. Conclusions: AI has the potential to transform cardio-oncology by shifting from reactive detection to proactive prevention of cardiotoxicity. Future research should prioritize multimodal integration, harmonized multicenter datasets, prospective validation, and guideline-based clinical trials. As emerging data are incorporated, the field is expanding rapidly—dynamic, complex, and evolving. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular and Stroke Imaging)
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