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. 2025 Feb 16;15(4):477.
doi: 10.3390/diagnostics15040477.

A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes

Luiza Camelia Nechita  1 , Dana Tutunaru  1 , Aurel Nechita  1 , Andreea Elena Voipan  2 , Daniel Voipan  2 , Anca Mirela Ionescu  3   4   5 , Teodora Simina Drăgoiu  3 , Carmina Liana Musat  1
Affiliations

A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes

Luiza Camelia Nechita et al. Diagnostics (Basel). .

Abstract

Background/Objectives: This study aimed to evaluate an artificial intelligence (AI)-enhanced electrocardiogram (ECG) screening protocol for improved accuracy, efficiency, and risk stratification across six sports: handball, football, athletics, weightlifting, judo, and karate. Methods: For each of the six sports, resting 12-lead ECGs from healthy children and junior athletes were analyzed using AI algorithms trained on annotated datasets. Parameters included the QTc intervals, PR intervals, and QRS duration. Statistical methods were used to examine each sport's specific cardiovascular adaptations and classify cardiovascular risk predictions as low, moderate, or high risk. Results: The accuracy, sensitivity, specificity, and precision of the AI system were 97.87%, 75%, 98.3%, and 98%, respectively. Among the athletes, 94.54% were classified as low risk and 5.46% as moderate risk with AI because of borderline abnormalities like QTc prolongation or mild T-wave inversions. Sport-specific trends included increased QRS duration in weightlifters and low QTc intervals in endurance athletes. Conclusions: The statistical analyses and the AI-ECG screening protocol showed high precision and scalability for the proposed athlete cardiovascular health risk status stratification. Additional early detection research should be conducted further for diverse cohorts of individuals engaged in sports and explore other diagnostic methods that can help increase the effectiveness of screening.

Keywords: artificial intelligence; athlete safety; cardiovascular screening; electrocardiogram (ECG); machine learning; predictive analytics; risk classification; risk prevention; sports medicine; statistics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Visual breakdown of participant categorization by sport type, discipline, and gender: (a) distribution of participants into team and individual sports; (b) breakdown of participants across specific sports; (c) gender distribution across all sports.
Figure 2
Figure 2
Workflow of the study methodology for cardiovascular assessment in young athletes.
Figure 3
Figure 3
The distribution of HR and the QT interval in the athlete cohort: (a) HR distribution by sport type; (b) QTc interval distribution by sport type.
Figure 4
Figure 4
QT interval variations: (a) across endurance, strength, and mixed sports categories; (b) across team and individual sports.
Figure 5
Figure 5
Age-related variations in QT interval and resting heart rate, with outliers highlighted.
Figure 6
Figure 6
Age-related variations in QT interval and resting heart rate with highlighted outliers across athletic cohorts.
Figure 7
Figure 7
Trends in QRS duration by age: (a) individual sports; (b) team sports.
Figure 8
Figure 8
Precision–recall curve illustrating the performance of AI models in ECG screening for athletes.
Figure 9
Figure 9
Distribution of low-risk classifications across sports.
Figure 10
Figure 10
Distribution of moderate-risk classifications across sports.
See this image and copyright information in PMC

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