Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs.

Background/Objectives: This study aimed to evaluate an artificial intelligence (AI)-enhanced electrocardiogram (ECG) screening protocol for improved accuracy, efficiency, and risk stratification across six sports: handball, football, athletics, weightlifting, judo, and karate. Methods: For each of the six sports, resting 12-lead ECGs from healthy children and junior athletes were analyzed using AI algorithms trained on annotated datasets. Parameters included the QTc intervals, PR intervals, and QRS duration. Statistical methods were used to examine each sport’s specific cardiovascular adaptations and classify cardiovascular risk predictions as low, moderate, or high risk. Results: The accuracy, sensitivity, specificity, and precision of the AI system were 97.87%, 75%, 98.3%, and 98%, respectively. Among the athletes, 94.54% were classified as low risk and 5.46% as moderate risk with AI because of borderline abnormalities like QTc prolongation or mild T-wave inversions. Sport-specific trends included increased QRS duration in weightlifters and low QTc intervals in endurance athletes. Conclusions: The statistical analyses and the AI-ECG screening protocol showed high precision and scalability for the proposed athlete cardiovascular health risk status stratification. Additional early detection research should be conducted further for diverse cohorts of individuals engaged in sports and explore other diagnostic methods that can help increase the effectiveness of screening.

A physically active lifestyle offers multiple benefits, including lowering the risk of cardiovascular disease, lowering body-mass index (BMI), and, last but not least, improving the quality of life. However, there are still disincentives to physical activity in children with heart diseases due to the high protection of parents and the scarcity of data in the literature. The purpose of this paper is to help pediatricians and pediatric cardiologists identify the type of physical activity allowed in children with congenital cardiac malformations, thus minimizing the risk of major adverse effects, such as acute coronary syndrome and sudden cardiac death. Therefore, we searched various electronic databases, such as PubMed, ScienceDirect, and Embase. We selected 61 articles published between 2008–2024. These articles included data on pediatric patients, from newborn to adolescent age. We decided to choose the 2008 study because of its focus on the essential role of education in schools regarding physical activity and the prevention of complications from sedentary lifestyles. Subsequently, we analyzed the data available in the literature up to 2024 regarding the type, intensity, and duration of exercise for patients with various congenital heart malformations. The conclusions of this review are presented based on the category of heart disease. There are differences in the free practice of sports in children with cardiomyopathies, atrioventricular block, arrhythmias with a genetic substrate, valvulopathies, and cyanogenic and non-cyanogenic congenital malformations. For example, children with arrhythmogenic right ventricle cardiomyopathy are not allowed to participate in sports competitions, but they can perform low physical activity for 150 min/week—golf, table tennis, and photography. However, it is recognized that a physically active lifestyle correlates with a decreased risk of cardiovascular diseases, body mass index, and an improvement in the quality of life. Children with congenital heart disease who are active have improved their cardiovascular systems.

Background: The electrocardiogram (ECG) screening in athletes is essential due to the unique cardiac adaptations induced by intensive training. However, differentiating between physiological adaptations and pathological abnormalities remains a significant challenge, particularly when considering variations across different sports, ages, and genders. Methods: A systematic review of observational studies published between 2015 and 2025 was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were extracted from 20 studies examining ECG changes in athletes across endurance, strength, and mixed sports, encompassing both adolescent and adult populations. Results: Commonly observed ECG changes included increased QRS amplitude, T-wave inversions, and sinus bradycardia, particularly in endurance athletes, while strength-based athletes frequently exhibited left ventricular hypertrophy. Male athletes showed higher QRS voltages, longer QRS durations, and higher PR intervals, whereas female athletes demonstrated elevated resting heart rates and prolonged corrected QT interval (QTc) intervals. Adolescents who engaged in regular sports displayed fewer abnormal ECG findings than adults; however, high-intensity training in adolescent athletes was associated with right atrial enlargement and increased P-wave duration. Detraining effectively reversed certain ECG changes, including prolonged QT intervals and T-wave abnormalities, though these changes often reappeared upon resumption of intense training. Notably, de novo ECG abnormalities, such as T-wave inversions and ST-segment depression, were identified in athletes with post-COVID-19 infections. This review also highlights the financial burden of widespread ECG screening, but reinforces the importance of ECG screening in preventing sudden cardiac death (SCD) through comprehensive cardiac evaluations. Conclusion: This review emphasizes the complexity of ECG interpretation in athletes, highlighting sport-specific, gender-based, and age-related variations. The persistent high false-positive rates underscore the need for refined, sport-specific ECG guidelines. Recent recognition of sports medicine as a primary specialty within the European Union (EU) reinforces the importance of comprehensive physician training. Integrating artificial intelligence (AI) technology into ECG screening can enhance diagnostic accuracy, reduce costs, and facilitate large-scale implementation. Meanwhile, collaborative efforts among clinicians, researchers, and policymakers are essential to developing cost-effective and standardized ECG screening protocols, ensuring improved athlete care, and advancing the field of sports cardiology.

Background/Objectives: Mental health screening in athletes is an essential process to support well-being and sustainable performance. The Athlete Psychological Strain Questionnaire (APSQ) represents the ten-item triage step of the Sport Mental Health Assessment Tool-1 (SMHAT-1), created by the International Olympic Committee. We aimed to gather relevant information concerning the validity of the APSQ in different cultural settings. Methods: The study was designed as a scoping review and included 19 articles from Scopus, PubMed, Embase, Web of Science, and Google Scholar databases. Articles were written in English and tested the APSQ validity. Results: Different studies used the original or the translated version of APSQ and tested its benchmarked validity against other validated questionnaires, ran confirmatory and exploratory analyses, test–retest stability, calculated diagnostic metrics, and internal consistency. Most studies agreed on the good internal consistency, with optimal Cronbach’s alpha values, test–retest reliability, three-factor solution, convergent validity with scales assessing distress, divergent validity with well-being scales as demonstrated by significant correlation coefficients. The cut-off showed good accuracy for anxiety and depressive symptoms in terms of AUC, sensitivity, and specificity, but, in some cases, a limited ability (based on the AUC) to detect sleep concerns, alcohol misuse, substance use, and disordered eating (as measured by BEDA-Q). Some authors suggested that using different cut-offs, including all questionnaires from SMHAT-1 Step 2, or using a clinical interview, might mitigate these concerns. Conclusions: Different cultural environments might influence the validity of APSQ. A structured translation and validation study is advised before implementing APSQ in a different language.

Background: Electrocardiographic (ECG) screening is crucial in pre-participation evaluations (PPEs) for elite athletes, aiding in the early detection of cardiac adaptations and potential risks. Elite female gymnasts experience unique cardiovascular adaptations due to intensive training, yet limited longitudinal data exist on their ECG evolution. This study introduces Oracle Crystal Ball, a predictive tool for forecasting ECG abnormalities and assessing PPE cost-effectiveness to optimize screening protocols. Methods: This retrospective cohort study analyzed ECG and cardiovascular parameters in twelve elite female gymnasts who underwent up to 14 PPEs over several years at the National Institute of Sports Medicine, Romania. Longitudinal ECG trends, training variables, and biochemical markers were examined using statistical analyses, including logistic regression, repeated measures ANOVA, and time-series forecasting (ARIMA). Monte Carlo simulations assessed the cost-effectiveness of 6-month vs. 12-month PPE schedules. Results: The athletes exhibited significant cardiovascular adaptations, including progressive declines in resting heart rate and training-induced ECG changes. Junctional escape rhythms and T-wave inversions (V1–V3) increased with age, requiring refined ECG interpretation. Predictive modeling demonstrated the feasibility of individualized risk stratification, while a cost-effectiveness analysis revealed that a 12-month PPE schedule was financially advantageous without reducing diagnostic accuracy. Conclusions: Longitudinal ECG monitoring and predictive analytics improve risk assessment in elite gymnasts. Oracle Crystal Ball enhances athlete-specific screening, minimizing unnecessary tests while ensuring early detection of clinically significant ECG changes. A 12-month PPE schedule is a cost-effective alternative for elite athletes.