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Application of Artificial Intelligence in Cardiology
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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 3880

Special Issue Editor

Dr. Matthias Unterhuber

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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
Special Issues, Collections and Topics in MDPI journals

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

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. Journal of Clinical Medicine 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
  • cardiology
  • clinical practice
  • valvular heart disease
  • arrhythmias
  • device therapy
  • machine learning

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Published Papers (5 papers)

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Research

17 pages, 1233 KB  
Article
The Application of Multimodal Data Fusion Algorithm MULTINet in Postoperative Risk Assessment of TAVR
by Wei He, Jiawei Luo and Xiaoyan Yang
J. Clin. Med. 2025, 14(24), 8620; https://doi.org/10.3390/jcm14248620 (registering DOI) - 5 Dec 2025
Abstract
Background: Transcatheter aortic valve replacement (TAVR) has emerged as a pivotal minimally invasive interventional therapy for aortic valve disease and has seen increasingly widespread clinical adoption in recent years. Despite its overall safety, the adverse events and even deaths in the postoperative period [...] Read more.
Background: Transcatheter aortic valve replacement (TAVR) has emerged as a pivotal minimally invasive interventional therapy for aortic valve disease and has seen increasingly widespread clinical adoption in recent years. Despite its overall safety, the adverse events and even deaths in the postoperative period still account for a certain percentage. Accurate identification of high-risk patients is therefore critical for optimizing preoperative decision making, guiding individualized treatment strategies and improving long-term outcomes. However, existing scoring systems and predictive models fail to fully leverage multimodal clinical data from patients, resulting in suboptimal predictive accuracy that falls short of the demands of precision medicine, indicating substantial room for improvement. Methods: In this study, a multimodal deep learning model named MULTINet (multimodal learning for TAVR risk network) was constructed using data from the MIMIC-IV (Medical Information Mart for Intensive Care) cohort. This model achieved unimodal and multimodal modeling through a dual-branch structure, and, by using an attention pooling fusion module, flexibly handled the input that contained missing modalities, to predict the 30-day all-cause mortality in TAVR patients. The area under the receiver operating characteristic curve (AUC), the area under the precision–recall curve (AUPR) and the recall rate were used for prediction evaluation. The calibration degree was evaluated by calibration diagrams and Brier scores, and its clinical practicability was assessed through decision curve analysis (DCA). And the integrated gradient method was used to identify key predictive features to enhance interpretability of the model. Results: In the postoperative 30-day all-cause mortality prediction task, the MULTINet method achieved an AUC value of 0.9153, AUPR value of 0.5708 and Recall value of 0.8051, which was significantly superior to the XGBoost method (AUC 0.8958, AUPR 0.4053 and Recall 0.7793) and the MedFuse method (AUC 0.5571, AUPR 0.2487 and Recall 0.3089). The MULTINet method demonstrated more robust and reliable probability estimation performance, with a Brier score of 0.0269, outperforming XGBoost (0.0343) and MedFuse (0.2496). It achieved a higher net benefit in decision analysis, reflecting its effectiveness in strategy optimization and actual decision-making benefits. The renal function, cardiac function and inflammation-related indicators contributed greatly in the prediction process. Conclusions: The multimodal deep learning model proposed in this study named MULTINet enables adaptive integration of multimodal clinical information for predicting all-cause mortality within 30 days post-TAVR, substantially improving both predictive accuracy and clinical applicability, providing robust support for clinical decision making and boosting TAVR management toward greater precision and intelligence. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Cardiology)
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15 pages, 1788 KB  
Article
Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention
by Łukasz Ledziński, Grzegorz Grześk, Michał Ziołkowski, Marcin Waligóra, Marcin Kurzyna, Tatiana Mularek-Kubzdela, Anna Smukowska-Gorynia, Ilona Skoczylas, Łukasz Chrzanowski, Piotr Błaszczak, Miłosz Jaguszewski, Beata Kuśmierczyk-Droszcz, Katarzyna Ptaszyńska, Katarzyna Mizia-Stec, Ewa Malinowska, Małgorzata Peregud-Pogorzelska, Ewa Lewicka, Michał Tomaszewski, Wojciech Jacheć, Michał Florczyk, Ewa Mroczek, Zbigniew Gąsior, Agnieszka Pawlak, Katarzyna Betkier-Lipińska, Piotr Pruszczyk, Olga Dzikowska-Diduch, Katarzyna Widejko, Judyta Winowska-Józwa and Grzegorz Kopećadd Show full author list remove Hide full author list
J. Clin. Med. 2025, 14(23), 8325; https://doi.org/10.3390/jcm14238325 - 23 Nov 2025
Viewed by 374
Abstract
Background/Objectives: Pulmonary arterial hypertension (PAH) is a complex cardiovascular disease with a high burden of morbidity and mortality. Although several risk prediction models have been proposed, the exact significance of distinct clinical parameters in predicting survival in PAH remains unclear. It is [...] Read more.
Background/Objectives: Pulmonary arterial hypertension (PAH) is a complex cardiovascular disease with a high burden of morbidity and mortality. Although several risk prediction models have been proposed, the exact significance of distinct clinical parameters in predicting survival in PAH remains unclear. It is important to emphasize that this study does not aim to validate or contradict existing clinical risk assessment calculators provided by the ESC or other scientific societies. Instead, the goal of this research is to identify and rank clinical parameters according to their importance in predicting mortality in PAH patients using machine learning techniques. Methods: Using the Database of Pulmonary Hypertension in the Polish population (BNP-PL) registry, 1755 adult patients with PAH were selected. Feature engineering was conducted using domain knowledge, guided by European Society of Cardiology (ESC) recommendations. Features were reduced using LASSO regression and sequential feature elimination algorithms. A classification model was built using the XGBoost algorithm, utilizing 17 features. The model was tested on a preselected subset of the BNP-PL data. The Shapley Additive Explanations (SHAP) method was used to explain the model’s predictions and to rank feature importance. Results: The model achieved satisfactory results across evaluated metrics, including an area under the curve of 0.767, accuracy of 0.738, specificity of 0.733, and sensitivity of 0.800. SHAP values effectively ranked the features, corroborating the significance of parameters present in the ESC risk stratification tables. Furthermore, local interpretation of results using SHAP enabled individualized assessment of feature importance, enhancing clinical applicability. Conclusions: The proposed artificial intelligence-based model demonstrates satisfactory predictive capability, highlighting the potential of machine learning techniques to support more personalized approaches to the management of PAH patients. This approach offers complementary insights into traditional risk assessment methods, providing clinicians with a novel tool for individualized risk evaluation and decision-making. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Cardiology)
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20 pages, 1915 KB  
Article
Feature Selection and Model Optimization for Survival Prediction in Patients with Angina Pectoris
by Róbert Bata, Amr Sayed Ghanem and Attila Csaba Nagy
J. Clin. Med. 2025, 14(22), 8111; https://doi.org/10.3390/jcm14228111 - 16 Nov 2025
Viewed by 462
Abstract
Background: With the rapid emergence of novel survival models and feature selection methods, comparing them with traditional approaches is essential to define contexts of optimal performance. Methods: This study systematically evaluates nine survival models combined with nine feature selection methods for predicting the [...] Read more.
Background: With the rapid emergence of novel survival models and feature selection methods, comparing them with traditional approaches is essential to define contexts of optimal performance. Methods: This study systematically evaluates nine survival models combined with nine feature selection methods for predicting the occurrence of angina pectoris using electronic health record (EHR) data from a Hungarian hospital (n = 29,655, features = 1150). Performance was assessed with the concordance index (C-index) and integrated Brier score (IBS) to compare predictive accuracy across methods. Results: Tree-based survival models, particularly gradient-boosted survival (GBS) and random survival forest (RSF), consistently outperformed conventional approaches in terms of C-index, but showed slightly worse calibration as reflected in their higher IBSs. The best-performing model was RSF, which was optimized using Bayesian hyperparameter tuning. For feature selection, tree-based methods such as Boruta and RSF-based approaches showed superior performance. We further identified clusters of feature selection methods and generated consensus feature sets. We also analyzed the internal relationships between the selected features. Survival model performance was also examined over time using the time-dependent Area Under the Curve (AUC) based on the best-performing feature set. Conclusions: Our findings highlight the substantial impact of recent methodological innovations in survival analysis, which offer significant gains in predictive accuracy and efficiency, ultimately support more robust clinical decision-making in the early identification of angina pectoris among patients with diabetes. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Cardiology)
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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 753
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 1611
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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