Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease.

Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model.

Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests
- EligibilityCriteria: Inclusion Criteria:  Athletes in need of cardiac or sports medical evaluation for the issuance of competitive eligibility. Enlisted athletes involved in sports like soccer or those with mixed or aerobic cardiovascular demands according to the COCIS 2017 classification. Aged 18 years or older but not exceeding 60 years. No history of cardiovascular disease. Signed Informed Consent.  Exclusion Criteria:  Athletes engaging in skill-based sports as per the COCIS 2017 classification. High clinical probability of cardiovascular disease, such as typical angina or heart failure. Pregnancy and/or breastfeeding (confirmed through self-declaration). - HealthyVolunteers: Accepts Healthy Volunteers - Gender: All - MinimumAge: 18 Years - MaximumAge: 60 Years - StdAgeList: Adult - StudyPopulation: Adults requiring medical evaluation for competitive sports eligibility - SamplingMethod: Non-Probability Sample