Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 531
- 试验地点
- 1
- 主要终点
- DL model accuracy
研究概览
简要总结
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
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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).
结局指标
主要结局
DL model accuracy
时间窗: 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.
次要结局
未报告次要终点