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Diagnostics
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Artificial Intelligence in Diagnosis and Management of Cardiovascular Diseases
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Artificial Intelligence in Diagnosis and Management of Cardiovascular Diseases

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 May 2026 | Viewed by 2268

Special Issue Editors

Dr. Corina Maria Vasile

E-Mail Website
Guest Editor
Department of Pediatric and Adult Congenital Cardiology, University Hospital of Bordeaux, 33600 Bordeaux, France
Interests: echocardiography; pediatric cardiology; congenital heart disease; congenital cardiopathy in adults; artificial intelligence; automatic measurements
Special Issues, Collections and Topics in MDPI journals
Dr. Xavier Iriart

E-Mail Website
Guest Editor
Department of Pediatric and Adult Congenital Cardiology, Hôpital Cardiologique du Haut-Lévêque, Centre Hospitalier Universitaire de Bordeaux, Bordeaux-Pessac, France
Interests: echocardiography; congenital heart disease; cardiac function; heart valve diseases; cardiac imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has shown great potential in the diagnosis and risk prediction of cardiovascular diseases. Deep learning and image recognition technology enable the automatic analysis of cardiovascular images available, making the identification of cardiac lesions quickly and accurate. This not only improves diagnostic efficiency, but also reduces the rate of misdiagnosis and missed diagnosis caused by human factors. Machine learning technology helps analyze huge amounts of clinical data and identify potential risk factors (such as high blood pressure, hyperlipidemia, diabetes, etc.) so that doctors can intervene in advance to reduce the risk of cardiovascular disease. AI also plays an important role in patient management and follow-up. Smart wearable devices and remote monitoring systems can monitor patients' heart rate, blood pressure and other physiological indicators in real time, detect abnormalities in time and notify doctors for intervention. This Special Issue aims to share the latest applications of AI technology in cardiovascular medicine. Original research articles and reviews are welcome.

Potential topics include, but are not limited to, the following:

  • AI in the automated diagnosis of cardiovascular diseases;
  • Deep learning techniques for cardiovascular imaging;
  • Machine learning for risk prediction and stratification in cardiovascular patients;
  • Integration of AI with electronic health records for cardiovascular care;
  • AI-driven personalized treatment plans and precision medicine in cardiology;
  • Use of natural language processing to analyze clinical notes and cardiovascular outcomes;
  • AI in predicting and managing heart failure and arrhythmias;
  • The role of AI in the early detection of congenital heart diseases;
  • AI-based decision support systems for cardiologists;
  • Applications of AI in interventional cardiology and surgery;
  • AI-powered remote monitoring and telemedicine in cardiovascular care;
  • Development and validation of AI algorithms in multi-center cardiovascular studies.

We look forward to receiving your contribution.

Dr. Corina Maria Vasile
Dr. Xavier Iriart
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

  • cardiovascular diseases
  • artificial intelligence
  • deep learning
  • machine learning
  • wearable devices
  • imaging techniques
  • personalized medicine
  • risk prediction
  • telemedicine
  • remote monitoring
  • ethical considerations

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 2557 KB  
Article
Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial Intelligence
by Melissa Valaee and Shahram Shirani
Diagnostics 2025, 15(19), 2471; https://doi.org/10.3390/diagnostics15192471 - 27 Sep 2025
Viewed by 585
Abstract
Background/Objectives: With a 17.9 million annual mortality rate, cardiovascular disease is the leading global cause of death. As such, early detection and disease diagnosis are critical for effective treatment and symptom management. Cardiac auscultation, the process of listening to the heartbeat, often [...] Read more.
Background/Objectives: With a 17.9 million annual mortality rate, cardiovascular disease is the leading global cause of death. As such, early detection and disease diagnosis are critical for effective treatment and symptom management. Cardiac auscultation, the process of listening to the heartbeat, often provides the first indication of underlying cardiac conditions. This practice allows for the identification of heart murmurs caused by turbulent blood flow. In this exploratory research paper, we propose an AI model to streamline this process to improve diagnostic accuracy and efficiency. Methods: We utilized data from the 2022 George Moody PhysioNet Heart Sound Classification Challenge, comprising phonocardiogram recordings of individuals under 21 years of age in Northeast Brazil. Only patients who had recordings from all four heart valves were included in our dataset. Audio files were synchronized across all recordings and converted to Mel spectrograms before being passed into a pre-trained Vision Transformer, and finally a MiniROCKET model. Additionally, data augmentation was conducted on audio files and spectrograms to generate new data, extending our total sample size from 928 spectrograms to 14,848. Results: Compared to the existing methods in the literature, our model yielded significantly enhanced quality assessment metrics, including Weighted Accuracy, Sensitivity, and F-Score, and resulted in a fast evaluation speed of 0.02 s per patient. Conclusions: The implementation of our method for the detection of heart murmurs can supplement physician diagnosis and contribute to earlier detection of underlying cardiovascular conditions, fast diagnosis times, increased scalability, and enhanced adaptability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Diagnosis and Management of Cardiovascular Diseases)
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11 pages, 396 KB  
Article
Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction
by Trevor Winger, Cagri Ozdemir, Shanti L. Narasimhan and Jaideep Srivastava
Diagnostics 2025, 15(6), 715; https://doi.org/10.3390/diagnostics15060715 - 13 Mar 2025
Cited by 3 | Viewed by 1092
Abstract
Background: Heart failure with reduced ejection fraction is a complex condition that necessitates adaptive, patient-specific management strategies. This study aimed to evaluate the effectiveness of a time-adaptive machine learning model, the Passive-Aggressive classifier, in predicting heart failure with reduced ejection fraction severity and [...] Read more.
Background: Heart failure with reduced ejection fraction is a complex condition that necessitates adaptive, patient-specific management strategies. This study aimed to evaluate the effectiveness of a time-adaptive machine learning model, the Passive-Aggressive classifier, in predicting heart failure with reduced ejection fraction severity and capturing individualized disease progression. Methods: A time-adaptive Passive-Aggressive classifier was employed, using clinical data and Brain Natriuretic Peptide levels as class designators for heart failure with reduced ejection severity. The model was personalized for individual patients by sequentially incorporating clinical visit data from 0–9 visits. The model’s adaptability and effectiveness in capturing individual health trajectories were assessed using accuracy and reliability metrics as more data were added. Results: With the progressive introduction of patient-specific data, the model demonstrated significant improvements in predictive capabilities. By incorporating data from nine visits, significant gains in accuracy and reliability were achieved, with the One-Versus-Rest AUC increasing from 0.4884 with no personalization (zero visits) to 0.8253 (nine visits). This demonstrates the model’s ability to handle diverse patient presentations and the dynamic nature of disease progression. Conclusions: The findings show the potential of time-adaptive machine learning models, particularly the Passive-Aggressive classifier, in managing heart failure with reduced ejection fraction and other chronic diseases. By enabling precise, patient-specific predictions, these approaches support early detection, tailored interventions, and improved long-term outcomes. This study highlights the feasibility of integrating adaptive models into clinical workflows to enhance the management of heart failure with reduced ejection fraction and similar chronic conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Diagnosis and Management of Cardiovascular Diseases)
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