Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 531
- Locations
- 1
- Primary Endpoint
- DL model accuracy
Study Overview
Brief Summary
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
Detailed Description
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.
The DL model requires training to be calibrated. The project plans to conduct accuracy evaluations on the validation population (76 athletes) and training trials on a different dataset (455 athletes).
There will be an initial phase of system training. Athletes will be assessed according to current guidelines and the italian cardiological guidelines for competitive sports participation - COCIS, with the required diagnostic tests on a case-by-case basis. At the end of the cardiac evaluation, athletes can be classified as "fit" or "unfit" for competitive activity.
Participants will submit the ECGs of "fit" and "unfit" athletes, categorized into these two groups, to a deep learning algorithm to train the artificial intelligence system.
A population of consecutive athletes will then be recruited to form the validation set for the test. These athletes have indications for evaluation for the granting of competitive fitness, as indicated by the referring sports physicians. In this case as well, athletes in the validation set will be assessed according to guidelines and COCIS with appropriate tests on a case-by-case basis to evaluate fitness for competition.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to 60 Years (Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
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).
Outcomes
Primary Outcomes
DL model accuracy
Time Frame: 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.
Secondary Outcomes
No secondary outcomes reported