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Diagnostics
Special Issues
Artificial Intelligence and Computational Methods in Cardiology 2025
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Artificial Intelligence and Computational Methods in Cardiology 2025

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: 30 April 2026 | Viewed by 911

Special Issue 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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 (1 paper)

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Research

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 405
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 2025)
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