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Diagnostics
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Artificial Intelligence and Computational Methods in Cardiology 2026
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Artificial Intelligence and Computational Methods in Cardiology 2026

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: closed (30 April 2026) | Viewed by 6770

Editors

Dr. Farhad Fathieh

E-Mail Website
Guest Editor
Analytics for Life, Toronto, ON M5X 1C9, Canada
Interests: diagnostic artificial intelligence in healthcare, machine learning, deep learning, medical devices; cardiovascular disease diagnostics
Special Issues, Collections and Topics in MDPI journals
Dr. Guennadi Saiko

E-Mail Website
Guest Editor
Department of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: cardiovascular disease; emergency care; optical modalities; diagnostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the application of artificial intelligence (AI) and computational techniques in cardiology, with a particular focus on diagnosing, monitoring, and screening cardiovascular diseases in real-world clinical scenarios. The overarching goal of this Special Issue is to showcase the rapid evolution of AI and computational methods in healthcare and their potential to enhance clinicians' capabilities for accurate diagnosis and treatment of cardiovascular disease, ultimately contributing to improved patient outcomes and advancements in cardiology.

The Special Issue invites submissions that demonstrate the utility of AI and computational methods in analyzing various multimodal signal sources and imaging modalities, including (but not limited to) optical signals, electrical signals (electroencephalogram, electromyogram, electrocardiogram), echocardiography, nuclear cardiology, cardiac computed tomography, and cardiac magnetic resonance imaging. Additionally, discussions on innovative wearable, point-of-care, and ambulatory devices incorporating AI applications for diagnosing cardiac diseases are encouraged. Submitted manuscripts should provide robust evidence of the outcomes' utility in real-life applications, including clear problem definitions, appropriate data usage, population selection, and adequate validation strategies. Furthermore, authors are expected to address the challenges and limitations inherent in these applications, ensuring a comprehensive and critical evaluation of the proposed methodologies.

We look forward to receiving your contributions.

Dr. Farhad Fathieh
Dr. Guennadi Saiko
Guest Editors

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 in cardiology
  • cardiovascular diseases
  • diagnostics, monitoring and screening
  • machine/deep learning
  • innovative medical devices
  • clinical applications
  • multimodal medical devices

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Published Papers (4 papers)

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Research

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33 pages, 5521 KB  
Article
Contrast-Free Myocardial Infarction Segmentation with Attention U-Net
by Khaled Ali Deeb, Yasmeen Alshelle, Hala Hammoud, Andrey Briko, Vladislava Kapravchuk, Alexey Tikhomirov, Amaliya Latypova and Ahmad Hammoud
Diagnostics 2026, 16(5), 768; https://doi.org/10.3390/diagnostics16050768 - 4 Mar 2026
Viewed by 678
Abstract
Background: Cardiovascular magnetic resonance (CMR) is the clinical gold standard for assessing cardiac anatomy and function. However, the manual segmentation of cardiac structures and myocardial infarction (MI) is time-consuming, prone to inter-observer variability, and often depends on contrast-enhanced imaging. Although deep learning (DL) [...] Read more.
Background: Cardiovascular magnetic resonance (CMR) is the clinical gold standard for assessing cardiac anatomy and function. However, the manual segmentation of cardiac structures and myocardial infarction (MI) is time-consuming, prone to inter-observer variability, and often depends on contrast-enhanced imaging. Although deep learning (DL) has enabled substantial automation, challenges remain in generalizability, particularly for MI detection from non-contrast cine CMR. Objective: This study proposes a comprehensive DL-based framework for automatic segmentation of cardiac structures and myocardial infarction using contrast-free cine CMR. Methods: The framework integrates multiple convolutional neural network (CNN) architectures for cardiac structure segmentation with an attention-based deep learning model for MI localization. Post-processing refinement using stacked autoencoders and active contour modeling is applied to improve anatomical consistency. Segmentation performance is evaluated using overlap-based and boundary-based metrics, including the Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD). Results: The best-performing model achieved Dice scores of 0.93 ± 0.05 for the left ventricular (LV) cavity, 0.89 ± 0.04 for the LV myocardium, and 0.91 ± 0.06 for the right ventricular (RV) cavity, with consistently low boundary errors across all structures. Myocardial infarction segmentation achieved a Dice score of 0.80 ± 0.02 with high recall, demonstrating reliable infarct localization without the use of contrast agents. Conclusions: By enabling accurate cardiac structure and myocardial infarction segmentation from contrast-free cine CMR, the proposed framework supports broader clinical applicability, particularly for patients with contraindications to gadolinium-based contrast agents and in emergency or resource-limited settings. This approach facilitates scalable, contrast-independent cardiac assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computational Methods in Cardiology 2026)
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21 pages, 1302 KB  
Article
Heart Sound Classification with MFCCs and Wavelet Daubechies Analysis Using Machine Learning Algorithms
by Sebastian Guzman-Alfaro, Karen E. Villagrana-Bañuelos, Manuel A. Soto-Murillo, Jorge Isaac Galván-Tejada, Antonio Baltazar-Raigosa, Angel Garcia-Duran, José María Celaya-Padilla and Andrea Acuña-Correa
Diagnostics 2026, 16(1), 83; https://doi.org/10.3390/diagnostics16010083 - 26 Dec 2025
Cited by 2 | Viewed by 1294
Abstract
Background/Objectives: Cardiovascular diseases are the leading cause of mortality worldwide according to the World Health Organization (WHO), highlighting the need for accessible tools for early detection. Automated classification systems based on signal processing and machine learning offer a non-invasive alternative to support clinical [...] Read more.
Background/Objectives: Cardiovascular diseases are the leading cause of mortality worldwide according to the World Health Organization (WHO), highlighting the need for accessible tools for early detection. Automated classification systems based on signal processing and machine learning offer a non-invasive alternative to support clinical diagnosis. Methods: This study implements and evaluates machine learning models for distinguishing normal and abnormal heart sounds using a hybrid feature extraction approach. Recordings labeled as normal, murmur, and extrasystolic were obtained from the PASCAL dataset and subsequently binarized into two classes. Multiple numerical datasets were generated through statistical features derived from Mel-Frequency Cepstral Coefficients (MFCCs) and Daubechies wavelet analysis. Each dataset was standardized and used to train four classifiers: support vector machines, logistic regression, random forests, and decision trees. Results: Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and area under curve. All classifiers achieved notable results; however, the support vector machine model trained with 26 MFCCs and Daubechies-4 wavelet coefficients obtained the best performance. Conclusions: These findings demonstrate that the proposed hybrid MFCC–Wavelet framework provides competitive diagnostic accuracy and represents a lightweight, interpretable, and computationally efficient solution for computer-aided auscultation and early cardiovascular screening. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computational Methods in Cardiology 2026)
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23 pages, 5751 KB  
Article
Automatic Diagnosis, Classification, and Segmentation of Abdominal Aortic Aneurysm and Dissection from Computed Tomography Images
by Hakan Baltaci, Sercan Yalcin, Muhammed Yildirim and Harun Bingol
Diagnostics 2025, 15(19), 2476; https://doi.org/10.3390/diagnostics15192476 - 27 Sep 2025
Viewed by 1901
Abstract
Background/Objectives: Diagnosis of abdominal aortic aneurysm and abdominal aortic dissection (AAA and AAD) is of strategic importance as cardiovascular disease has fatal implications worldwide. This study presents a novel deep learning-based approach for the accurate and efficient diagnosis of abdominal aortic aneurysms [...] Read more.
Background/Objectives: Diagnosis of abdominal aortic aneurysm and abdominal aortic dissection (AAA and AAD) is of strategic importance as cardiovascular disease has fatal implications worldwide. This study presents a novel deep learning-based approach for the accurate and efficient diagnosis of abdominal aortic aneurysms (AAAs) and aortic dissections (AADs) from CT images. Methods: Our proposed convolutional neural network (CNN) architecture effectively extracts relevant features from CT scans and classifies regions as normal or diseased. Additionally, the model accurately delineates the boundaries of detected aneurysms and dissections, aiding in clinical decision-making. A pyramid scene parsing network has been built in a hybrid method. The layer block after the classification layer is divided into two groups: whether there is an AAA or AAD region in the abdominal CT image, and determination of the borders of the detected diseased region in the medical image. Results: In this sense, both detection and segmentation are performed in AAA and AAD diseases. Python programming has been used to assess the accuracy and performance results of the proposed strategy. From the results, average accuracy rates of 83.48%, 86.9%, 88.25%, and 89.64% were achieved using ResDenseUNet, INet, C-Net, and the proposed strategy, respectively. Also, intersection over union (IoU) of 79.24%, 81.63%, 82.48%, and 83.76% have been achieved using ResDenseUNet, INet, C-Net, and the proposed method. Conclusions: The proposed strategy is a promising technique for automatically diagnosing AAA and AAD, thereby reducing the workload of cardiovascular surgeons. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computational Methods in Cardiology 2026)
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Review

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21 pages, 1214 KB  
Review
Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework
by José Ferreira Santos and Hélder Dores
Diagnostics 2026, 16(3), 390; https://doi.org/10.3390/diagnostics16030390 - 26 Jan 2026
Cited by 1 | Viewed by 1626
Abstract
Background: Large language models (LLMs) are becoming progressively integrated into clinical practice; however, their role in cardiovascular (CV) prevention remains unclear. This review synthesizes current evidence on LLM applications in preventive cardiology and proposes a governance framework for their safe translation into practice. [...] Read more.
Background: Large language models (LLMs) are becoming progressively integrated into clinical practice; however, their role in cardiovascular (CV) prevention remains unclear. This review synthesizes current evidence on LLM applications in preventive cardiology and proposes a governance framework for their safe translation into practice. Methods: We conducted a comprehensive narrative review of literature published between January 2015 and November 2025. Evidence was synthesized across three functional domains: (1) patient applications for health literacy and behavior change; (2) clinician applications for decision support and workflow efficiency; and (3) system applications for automated data extraction, registry construction, and quality surveillance. Results: Evidence suggests that while LLMs generate empathetic, guideline-concordant patient education, they lack the nuance required for unsupervised, personalized advice. For clinicians, LLMs effectively summarize clinical notes and draft documentation but remain unreliable for deterministic risk calculations and autonomous decision-making. System-facing applications demonstrate potential for automated phenotyping and multimodal risk prediction. However, safe deployment is constrained by hallucinations, temporal obsolescence, automation bias, and data privacy concerns. Conclusions: LLMs could help mitigate structural barriers in CV prevention but should presently be deployed only as supervised “reasoning engines” that augment, rather than replace, clinician judgment. To guide the transition from in silico performance to bedside practice, we propose the C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, and Ongoing vigilance) as a roadmap for responsible integration. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computational Methods in Cardiology 2026)
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