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.

Sports medicine is a medical specialty that supports the performance of professional and amateur athletes while maintaining their health. Sports medicine professionals need to ensure the safe participation of athletes in sports activities achieved through a periodical preparticipation evaluation (PPE) and a regular medical monitoring of the athletes’ health in accordance with the latest recommendations regarding health condition and medical history, physical working capacity, training period and programme, recovery, nutrition, use of supplements, injuries prevention and safe return to play. In order to harmonise these national variations in the content and application of the PPE, the EFSMA Scientific and Educational Commission proposes a ‘gold standard’ for elite athletes across Europe. Important objectives of PPE are early detection and prevention of severe complications during sports activities both in leisure time and competitive sports. The PPE should entail the following diagnostic components: health status, anthropometry, functional and exercise capacity. It is of utmost importance to develop and implement preventive strategies such as the PPE. Besides monitoring the health status of athletes, the PPE plays an important role in the selection process, bringing valuable information for coaches and supporting a personalised treatment approach. Screening of athletes through a standardised digital PPE could be beneficial for a better understanding of the impact of long-term physical activity. Furthermore, PPE leads the scientific community to a way of working closer together in the interest of the athletes.

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.