Abdulraheem Lubabat Wuraola, Baraah Al-Dwa, Dmitry Shchekochikhin, Daria Gognieva, Petr Chomakhidze, Natalia Kuznetsova, Philipp Kopylov, Afina A Bestavashvilli
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A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.
Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is due to the growing need to improve efficiency in diagnostics and hasten the process of delivering treatment. Several institutions have actively assessed the possibility of creating algorithms for advancing our understanding of atrial fibrillation (AF), a common form of sustained arrhythmia. This means that artificial intelligence is now being used to analyze electrocardiogram (ECG) data. The data is typically extracted from large patient databases and then subsequently used to train and test the algorithm with the help of neural networks. Machine learning has been used to effectively detect atrial fibrillation with more accuracy than clinical experts, and if applied to clinical practice, it will aid in early diagnosis and management of the condition and thus reduce thromboembolic complications of the disease. In this text, a review of the application of machine learning in the analysis and detection of atrial fibrillation, a comparison of the outcomes (sensitivity, specificity, and accuracy), and the framework and methods of the studies conducted have been presented.
期刊介绍:
Current Cardiology Reviews publishes frontier reviews of high quality on all the latest advances on the practical and clinical approach to the diagnosis and treatment of cardiovascular disease. All relevant areas are covered by the journal including arrhythmia, congestive heart failure, cardiomyopathy, congenital heart disease, drugs, methodology, pacing, and preventive cardiology. The journal is essential reading for all researchers and clinicians in cardiology.