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Application of Artificial Intelligence in Cardiology

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: 23 January 2026 | Viewed by 1968

Special Issue Editor


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Guest Editor
1. Department of Cardiology, Hospital of Bolzano (SABES-ASDAA), Bolzano, Italy
2. Teaching Hospital of Paracelsus Medical University; Strubergasse 21, 5020 Salzburg, Austria
Interests: interventional and clinical cardiology; machine learning; artificial intelligence
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is rapidly evolving, with significant research and growing interest in its applications. As one of the most talked-about tools today, its swift development and wide-ranging potential present both exciting opportunities and challenges. In the field of cardiology, where cutting-edge technology intersects with evidence-based and hands-on experience medicine, AI holds immense promise. However, it is our responsibility to shed light on its practical and clinical impact with possible consequences.

This Special Issue of the Journal of Clinical Medicine will explore current AI use cases in cardiology, examining both its benefits and challenges. We invite authors to submit papers that investigate the use of AI in cardiology, focusing on its clinical applications, practical implementation, and the future direction of AI in this vital field. Through this collection, we aim to provide a comprehensive look at AI’s role in shaping the future of cardiology, starting from its limitations to further widening its capabilities and our understanding.

Dr. Matthias Unterhuber
Guest Editor

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Keywords

  • artificial intelligence
  • cardiology
  • clinical practice
  • valvular heart disease
  • arrhythmias
  • device therapy
  • machine learning

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Published Papers (2 papers)

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Research

13 pages, 1960 KB  
Article
Deep Learning-Based Risk Assessment and Prediction of Cardiac Outcomes Using Single-Lead 24-Hour Holter-ECG in Patients with Heart Failure or Myocardial Infarction
by Ju Youn Kim, Kyung Geun Kim, Sunghoon Joo, Mineok Chang, Juwon Kim, Kyoung-Min Park, Young Keun On, June Soo Kim, Young Soo Lee and Seung-Jung Park
J. Clin. Med. 2025, 14(20), 7209; https://doi.org/10.3390/jcm14207209 - 13 Oct 2025
Viewed by 327
Abstract
Background: Deep learning (DL) models using Holter-ECG may enhance risk stratification after heart failure (HF) or myocardial infarction (MI). Objective: To evaluate the prognostic performance of a Holter-based DL model for predicting major adverse cardiac events (MACE), compared with conventional noninvasive markers. Methods: [...] Read more.
Background: Deep learning (DL) models using Holter-ECG may enhance risk stratification after heart failure (HF) or myocardial infarction (MI). Objective: To evaluate the prognostic performance of a Holter-based DL model for predicting major adverse cardiac events (MACE), compared with conventional noninvasive markers. Methods: In the K-REDEFINE study, 1108 patients with acute MI or HF underwent 24 h Holter monitoring. A DL model was trained using raw Holter-ECG data and tested for predicting a composite of cardiac death and ventricular arrhythmias. Its performance was compared with heart rate turbulence (HRT), T-wave alternans (TWA), and ejection fraction (EF). Results: During follow-up, 56 adjudicated cardiac deaths (1.18%/yr) and 21 ventricular arrhythmias (0.44%/yr) occurred. The DL model showed an area under the receiver operating characteristic curve (AUROC) of 0.74 (95% CI, 0.70–0.77) for the composite outcome, improving to 0.77 (0.74–0.81) when combined with EF. In comparison, HRT and TWA showed lower AUROCs of 0.62 and 0.55, respectively. For cardiac death alone, the AUROC reached 0.79, further improving to 0.82 with EF. Model-derived risk stratification revealed a seven-fold increase in cardiac death risk in the high-risk group compared to the low-risk group (HR 7.47, 95% CI 2.24–24.96, p < 0.001). This stratification remained particularly effective in patients with EF > 40%. Conclusions: A DL algorithm trained on single-lead Holter-ECG data effectively predicted cardiac death and ventricular arrhythmia. Its performance surpassed conventional markers and was further enhanced when integrated with EF, supporting its potential for noninvasive, scalable risk stratification. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Cardiology)
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11 pages, 982 KB  
Article
An Artificial Intelligence Algorithm for Early Detection of Left Ventricular Systolic Dysfunction in Patients with Normal Sinus Rhythm
by Seongjin Park, Hyo Jin Lee, Sung-Hee Song, KyungChang Woo, Jiwon Kim, Juwon Kim, Ju Youn Kim, Seung-Jung Park, Young Keun On and Kyoung-Min Park
J. Clin. Med. 2025, 14(12), 4257; https://doi.org/10.3390/jcm14124257 - 15 Jun 2025
Cited by 1 | Viewed by 1152
Abstract
Background/Objectives: Most previous studies using artificial intelligence (AI) to detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs) relied on data obtained near the time of echocardiography or included patients with known cardiac disease, limiting their specificity for screening. We aimed to evaluate [...] Read more.
Background/Objectives: Most previous studies using artificial intelligence (AI) to detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs) relied on data obtained near the time of echocardiography or included patients with known cardiac disease, limiting their specificity for screening. We aimed to evaluate whether AI models could predict future LVSD from ECGs interpreted as normal and recorded one to two years before echocardiography. Methods: We retrospectively analyzed 24,203 sinus rhythm ECGs from 11,131 patients. Two convolutional neural network models (DenseNet-121 and ResNet-101) were trained (70%), validated (10%), and tested (20%) to predict LVSD (defined as ejection fraction ≤50%). Survival analysis was performed using Kaplan–Meier curves and the log-rank test. Results: Of the total population, 2734 patients had LVSD and 8397 had preserved EF. DenseNet-121 and ResNet-101 demonstrated excellent discrimination for LVSD with AUROCs of 0.930 and 0.925, accuracies of 0.887 and 0.860, sensitivities of 0.821 and 0.856, and specificities of 0.908 and 0.861, respectively. In the test set, patients predicted to have LVSD showed a significantly higher risk of echocardiographic LVSD (hazard ratio 9.89, 95% CI 8.20–11.92, p = 0.005) and lower 24-month survival (log-rank p < 0.001). Conclusions: AI-enabled ECG models predicted future LVSD from clinically normal ECGs recorded up to two years prior to imaging. These findings suggest a potential role for AI-ECG in the early detection of subclinical LVSD and improved risk stratification in asymptomatic individuals. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Cardiology)
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