Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility

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Conditions studied: Sports Cardiology, Preventive Cardiology, Electrocardiogram, Artificial Intelligence

In brief

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

Key facts

Study ID
NCT06285084
Run by
I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio
People needed
531
Starts
2024-02-02
Expected to finish
2027-02-02
Last updated by the study team
2024-02-29

Who can join

Age: 18 and older, up to 60. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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