Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-60
SponsorI.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio
About this trial
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 criteria
Qualifiers
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.
Disqualifiers
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).
Trial design
Treatments tested in this trial
- Not listed
Trial groups
531 Participants
are grouped into 2 trial groupsSponsors and collaborators
Source ClinicalTrials.gov