Background: Intense training exercise regimes cause physiological changes within the heart to help cope with the increased stress, known as the "athlete's heart". These changes can mask pathological changes, making them harder to diagnose and increasing the risk of an adverse cardiac outcome.
Aim: This paper reviews which machine learning techniques (ML) are being used within athlete's heart research and how they are being implemented, as well as assesses the uptake of these techniques within this area of research.
Methods: Searches were carried out on the Scopus and PubMed online datasets and a scoping review was conducted on the studies which were identified.
Results: Twenty-eight studies were included within the review, with ML being directly referenced within 16 (57%). A total of 12 different techniques were used, with the most popular being artificial neural networks and the most common implementation being to perform classification tasks. The review also highlighted the subgroups of interest: predictive modelling, reviews, and wearables, with most of the studies being attributed to the predictive modelling subgroup. The most common type of data used was the electrocardiogram (ECG), with echocardiograms being used the second most often.
Conclusion: The results show that over the last 11 years, there has been a growing desire of leveraging ML techniques to help further the understanding of the athlete's heart, whether it be by expanding the knowledge of the physiological changes or by improving the accuracies of models to help improve the treatments and disease management.
Keywords: athlete’s heart; cardiology; echocardiography; electrocardiography; machine learning; pre-participation screening.