Actively Recruiting
Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Led by I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio · Updated on 2024-02-29
531
Participants Needed
1
Research Sites
156 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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
CONDITIONS
Official Title
Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Athletes needing cardiac or sports medical evaluation for competitive eligibility
- Involved in sports like soccer or those with mixed or aerobic cardiovascular demands as per COCIS 2017 classification
- Aged 18 years or older and not older than 60 years
- No history of cardiovascular disease
- Signed informed consent
You will not qualify if you...
- Athletes participating only in skill-based sports according to COCIS 2017 classification
- High clinical probability of cardiovascular disease, such as typical angina or heart failure
- Pregnant or breastfeeding (self-declared)
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Ospedale Galeazzi-Sant'Ambrogio
Milan, Lombardy, Italy, 20157
Actively Recruiting
Research Team
D
Davide Marchetti, MD
CONTACT
D
Daniele Andreini, MD, PhD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
2
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