Atrial Fibrillation (AF) is a heritable arrhythmia related to large morbidity, including stroke, coronary heart failure, dementia, and mortality. One out of three people at excessive hazard of developing AF might also additionally allow early detection of cardiac rhythm tracking and treatment, or behavioral change to save you AF altogether. Understanding the biological basis of risk estimation from machine learning models can be helpful. Model interpretability, rationalization of model output, promotion of clinician confidence, and potential. Allows identification of individuals with specific mechanical signaling pathways leading to atrial fibrillation. We recently developed AI algorithms have been validated to predict the 5-year risk of developing atrial fibrillation. Use the 12-lead ECG (“ECGAI”). 7 In this study, we performed a genetic relevance test. Assess the genetic basis using AF risk estimates generated from the ECGAI model. It will be reflected in the output. As a comparative test, we evaluated the genetic basis of what has been widely validated. Cardiovascular disease is the leading cause of death in the world1 and Electrocardiography (ECG) is an important tool in its diagnosis. With the transition from analog ECG to digital ECG, automated computer analysis of standard 12-lead ECG has become more important in the medical diagnostic process. However, the limited performance of traditional algorithms hinders its use as a stand-alone diagnostic tool and leaves it to a secondary role. Deep Neural Networks (DNN) has recently achieved remarkable success in tasks such as image classification and speech recognition, and there are great expectations for how this technology can improve healthcare delivery and clinical practice. To date, the most successful applications have used supervised learning capabilities to automate the diagnosis of exams. A supervised learning model that learns to map inputs to outputs based on exemplary input / output pairs is a human in everyday workflow in diagnosing breast cancer and detecting retinal disease using a 3D optical coherent tomography scan specialist. Demonstrated better performance than. Although efficient, training DNNs with this setting requires a large amount of labeled data. This poses several challenges for medical applications, such as those related to the confidentiality and security of personal health information.

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