Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.
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Ошибки и ограничения
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Palermi Stefano, Zeidaabadi Boroumand, Vecchiato Marco, Anselmino Matteo, Adorisio Rachele, Biffi Alessandro, Borrelli Francesco, Brugin Erica, Cantarutti Nicoletta, Cavarretta Elena, Cominacini Mattia, Corsi Marco, DAscenzi Flavio, De Feo Vittorio, Di Gioia Giuseppe, Dorelli Gianluigi, Foccardi Giulia, Gallina Sabina, Giangrandi Silvia, Graziano Francesca, Aggour Hesham, El-Medany Ahmed, Pastika Libor, Barker Joseph, Patlatzoglou Konstantinos, Khattak Gul Rukh, Lodi Elisa, Livio Alberto, Maestrini Viviana, Manfredi Guglielmo Leonardo, Mansour Davide, Modena Mariagrazia, Neunhaeuserer Daniel, Nigro Antonia, Palermi Andrea, Pellegrino Alessio, Pelliccia Antonio, Quattrini Filippo Maria, Ricci Fabrizio, Scarzella Fiammetta, Squeo Maria Rosaria, Tonelli Riccardo, Zanardo Emanuele, Zorzi Alessandro, Peters Nicholas S, Kramer Daniel B, Waks Jonathan W, De Ferrari Gaetano Maria, Ng Fu Siong, Sau Arunashis, Saglietto Andrea
Аннотация
OBJECTIVES: To develop and externally validate a deep learning (DL)-based AI-ECG ensemble model for detecting imaging-confirmed structural heart disease in competitive athletes undergoing PPS.
METHODS: A convolutional neural network (CNN) ensemble was trained using hospital-derived ECG images from Beth Israel Deaconess Medical Center (BIDMC, Boston, USA) and externally validated in the Italian Team for Athlete CARDiac evaluation and AI-based Risk prediction (ITACARD-AI) registry. Separate CNNs were developed for valvular heart disease (VHD) and cardiomyopathies (CM) and combined using XGBoost meta-learning. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), subgroup analyses, and threshold-based evaluation.
RESULTS: The ITACARD-AI cohort included 1115 competitive athletes (mean age 26 ± 13 years; 70% male), including 48 athletes (4.3%) with VHD and 30 (2.7%) with CM. External validation demonstrated substantial performance degradation compared with hospital-based internal validation. AUROC values decreased to 0.70 (95% CI 0.64-0.75) for VHD and 0.69 (95% CI 0.60-0.78) for CM, indicating only modest discrimination in the screening population. Threshold analyses showed high negative predictive values (∼99%) but persistently low positive predictive values (≤8%), reflecting limited disease enrichment and strong prevalence dependence.
CONCLUSIONS: Hospital-trained AI-ECG models demonstrated limited transportability to real-world athlete screening populations. The marked reduction in external performance suggests that low disease prevalence, heterogeneous physiological remodeling, and incomplete ECG expression of structural abnormalities may substantially limit class separability in PPS environments. Although AI-ECG may provide cautious adjunctive support within physician-led workflows, these findings highlight the intrinsic challenges of applying ECG-based AI to low-prevalence sports cardiology screening populations.