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Artificial Intelligence (AI) in Cardiovascular Medicine
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Artificial Intelligence (AI) in Cardiovascular Medicine

A special issue of Medical Sciences (ISSN 2076-3271). This special issue belongs to the section "Cardiovascular Disease".

Deadline for manuscript submissions: 1 October 2026 | Viewed by 512

Special Issue Editors

Dr. Nilima Rajpal Kundnani

E-Mail Website
Guest Editor
1. Prevention and Cardiovascular Recovery, Department VI-Cardiology, University Clinic of Internal Medicine and Ambulatory Care, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
2. Research Centre of Timisoara Institute of Cardiovascular Diseases, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania.
Interests: preventive medicine; cardiology; AI; evidence based medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Mihaela Adela Iancu

E-Mail Website
Guest Editor
IInd Family Medicine Department, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
Interests: cardiology; family medicine; preventive medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, underscoring the urgent need for strategies that enable early detection and timely intervention. Recent advances in artificial intelligence (AI) and machine learning offer transformative opportunities to improve cardiovascular health by enabling more accurate, efficient, and scalable diagnostic tools. From the automated analysis of imaging and electrocardiographic data to predictive modeling using electronic health records and wearable technologies, AI is reshaping how clinicians identify risk factors, detect early pathological changes, and guide personalized treatment strategies.

This Special Issue invites contributions that showcase innovative AI methodologies for early CVD detection, risk stratification, and prognosis. Topics of interest include—but are not limited to—deep learning for medical imaging, AI-powered biomarkers, data integration across multimodal sources, predictive analytics, explainable AI in clinical practice, and applications in digital health and remote monitoring. Both methodological advances and clinically oriented studies are welcome. By bringing together research at the intersection of AI and cardiovascular medicine, this Special Issue aims to accelerate progress toward earlier diagnosis and improved patient outcomes.

Dr. Nilima Rajpal Kundnani
Dr. Mihaela Adela Iancu
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medical Sciences is an international peer-reviewed open access quarterly 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 1600 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
  • patient care
  • preventive cardiology
  • artificial intelligence
  • machine learning

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Published Papers (1 paper)

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Research

11 pages, 548 KB  
Article
Risk of Chronic Kidney Disease and Implications in Patients with Atrial Fibrillation for the Development of Major Adverse Cardiovascular Events with Machine Learning
by Pedro Moltó-Balado, Josep-Lluís Clua-Espuny, Carlos Tarongi-Vidal, Paula Barrios-Carmona, Victor Alonso-Barberán, Maria-Teresa Balado-Albiol, Andrea Simeó-Monzó, Jorge Canela-Royo and Alba del Barrio-González
Med. Sci. 2025, 13(4), 289; https://doi.org/10.3390/medsci13040289 - 27 Nov 2025
Viewed by 303
Abstract
Background: Atrial fibrillation (AF) and chronic kidney disease (CKD) often overlap and may amplify cardiovascular risk. Whether renal dysfunction should be incorporated into composite cardiovascular endpoints in AF remains uncertain. We aimed to quantify AF-associated risk of MACE and evaluate the incremental prognostic [...] Read more.
Background: Atrial fibrillation (AF) and chronic kidney disease (CKD) often overlap and may amplify cardiovascular risk. Whether renal dysfunction should be incorporated into composite cardiovascular endpoints in AF remains uncertain. We aimed to quantify AF-associated risk of MACE and evaluate the incremental prognostic value of kidney measures (eGFR and albuminuria) to inform composite outcomes and clinical management. Methods: We performed a retrospective, community-based cohort study of 40,297 adults aged 65–95 years. Individuals with incident AF (n = 2574) were followed for 5 years. MACE and components were ascertained from linked health records; only events after AF diagnosis were analyzed. Cox models estimated adjusted hazard ratios (HRs). Risk was further stratified by eGFR stages and urine albumin-to-creatinine ratio (UACR) categories. Exploratory machine learning (ML) was developed to predict MACE in patients with AF and CKD, with model interpretability assessed by feature importance analysis. Results: Incident AF was associated with higher risk of MACE (HR 3.52), CKD (HR 1.97) and all-cause mortality (HR 1.14). CKD was nearly twice more frequent in AF than in non-AF (30.9% vs. 14.5%; p < 0.001). Among patients with AF, a graded eGFR–risk relationship was observed: compared with higher eGFR, MACE risk increased across G3a–G5, peaking in G5 (HR 2.08). Albuminuria showed a parallel gradient: versus UACR <30 mg/g, UACR 30–299 mg/g and ≥300 mg/g were associated with an increased risk of MACE (HR 1.51 and 1.76, respectively). Conclusions: Newly diagnosed AF confers a substantial excess risk of MACE and its components. The consistent eGFR and albuminuria in AF support considering clinically meaningful renal endpoints within composite outcomes and prioritizing integrated cardiorenal management. These findings provide actionable evidence to refine risk stratification and endpoint selection in AF research and care. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)
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