What question did this study set out to answer?

This review aims to assess the effectiveness and limitations of AI and machine learning in distinguishing benign cardiovascular adaptations from serious pathologies in athletes.

January 10, 2026International Journal of Innovative Technologies in Social Science0 citationsOpen Access

The Transformation of Cardiovascular Screening in Athletes: The Multimodal Role of Artificial Intelligence in Differentiating Cardiac Pathologies From Physiological Adaptation

WKWeronika KozakUniversity of Warmia and Mazury in Olsztyn AGAleksandra GrygorowiczMedical University of Warsaw KBKlaudia BaranMedical University of Warsaw

Key Result

Deep learning models applied to ECG have over 90% sensitivity in detecting occult cardiomyopathies, outperforming traditional diagnostic methods.

Key Points

This review aims to assess the effectiveness and limitations of AI and machine learning in distinguishing benign cardiovascular adaptations from serious pathologies in athletes.
Conducted systematic literature search across PubMed, Scopus, and Web of Science from 2000 to 2025.
Synthesized findings from 48 key studies on AI algorithms used for cardiovascular diagnosis in athletes.
Compared AI methodologies, including deep learning applied to ECG and automated imaging, to traditional diagnostic criteria.
Deep learning models show over 90% sensitivity in identifying occult cardiomyopathies, outperforming traditional methods.
AI-enhanced imaging improves tissue characterization reproducibility significantly.
Algorithms analyzing phonocardiograms perform comparably to echocardiography in detecting valvular disease.

Structured PICO

Does the use of Artificial Intelligence and Machine Learning algorithms improve the diagnostic accuracy of differentiating cardiac pathologies from physiological adaptation in competitive athletes?

Population

Competitive athletes undergoing cardiovascular screening for the differentiation of benign physiological adaptations ('athlete's heart') from potentially lethal pathologies (synthesized from 48 key studies).

Intervention

Artificial Intelligence (AI) and Machine Learning (ML) algorithms, specifically deep learning applied to electrocardiography (AI-ECG), automated imaging analysis (Echocardiography, CMR), and digital auscultation.

Comparator

Standard clinical criteria and traditional diagnostic methods (human interpretation).

Outcome

Diagnostic performance metrics (sensitivity, specificity, AUC) for the detection of Hypertrophic Cardiomyopathy (HCM), Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC), valvular anomalies, and inherited channelopathies.

Multimodal AI applications, including AI-ECG and automated imaging, offer highly sensitive and scalable tools for differentiating life-threatening cardiomyopathies from benign physiological adaptations in athletes, though clinical implementation requires athlete-specific training data.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Purpose: The primary objective of this review is to evaluate the efficacy, clinical applications, and current limitations of Artificial Intelligence (AI) and Machine Learning (ML) in diagnosing cardiovascular diseases (CVD) among competitive athletes. Specifically, this study addresses the critical diagnostic challenge of differentiating benign physiological adaptations known as "athlete's heart" from potentially lethal pathologies, including cardiomyopathies and channelopathies, to prevent sudden cardiac death. Materials and Methods: A systematic literature search was conducted across PubMed, Scopus, and Web of Science databases covering the period from 2000 to 2025. The review identified and synthesized 48 key studies utilizing AI algorithms—specifically deep learning applied to electrocardiography (AI-ECG) and automated imaging analysis (Echocardiography, CMR)—for the detection of Hypertrophic Cardiomyopathy (HCM), Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC), valvular anomalies, and inherited channelopathies. Diagnostic performance metrics were analyzed to compare AI methodologies against standard clinical criteria. Results: Deep learning models applied to ECG demonstrate superior sensitivity (>90%) in detecting occult cardiomyopathies compared to traditional methods , while AI-enhanced imaging significantly improves the reproducibility of tissue characterization. AI algorithms, such as those analyzing phonocardiograms, show efficacy comparable to echocardiography in detecting valvular heart disease. Conclusions: AI represents a paradigm shift in sports cardiology, offering potential for scalable and cost-effective screening. However, widespread clinical implementation is currently hindered by the "black box" nature of algorithms and the scarcity of large, athlete-specific training datasets. Future deployment requires explainable AI models validated on diverse athletic cohorts.

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