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
Eligibility
Sex
ALL
Min age
18 Years
Max age
60 Years
Healthy volunteers
Accepted
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).
Primary outcome measure(s)
DL model accuracy — From first medical evaluation with ECG until the final medical decision on competitive sports eligibility, up to 12 months The accuracy of the DL model in recognizing the ECGs of athletes deemed fit or unfit will be evaluated by comparing the results with those obtained from the assessment performed by the sports physician (gold standard).
Participants will categorize the athletes into true positives, false positives, true negatives, and false negatives.
To define the ability of the DL model to discriminate between ECGs of athletes deemed fit or unfit, the receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) will be calculated.
Trial sites (1)
Facility
City
Region
Status
Ospedale Galeazzi-Sant'Ambrogio
Milan
Lombardy
Recruiting
More I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio trials in Italy
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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