Hisaki Makimoto, Takeru Shiraga, Benita Kohlmann, Christofori Eleni Magnisali, Shqipe Gerguri, Nobuaki Motoyama, Lukas Clasen, Alexandru Bejinariu, Kathrin Klein, Asuka Makimoto, Christian Jung, Ralf Westenfeld, Tobias Zeus, Malte Kelm
AIMS: The medical need for screening of aortic valve stenosis (AS), which leads to timely and appropriate medical intervention, is rapidly increasing because of the high prevalence of AS in elderly population. This study aimed to establish a screening method using understandable artificial intelligence (AI) to detect severe AS based on heart sounds and to package the built AI into a smartphone application. METHODS AND RESULTS: In this diagnostic accuracy study, we developed multiple convolutional neural networks (CNNs) using a modified stratified five-fold cross-validation to detect severe AS in electronic heart sound data recorded at three auscultation locations...
June 2022: European heart journal. Digital health