Efficient screening for severe aortic valve stenosis using understandable artificial intelligence: a prospective diagnostic accuracy study

• Published in: European heart journal. Digital health Vol. 3; no. 2; pp. 141 - 152
• Main Authors: Makimoto, Hisaki, Shiraga, Takeru, Kohlmann, Benita, Magnisali, Christofori Eleni, Gerguri, Shqipe, Motoyama, Nobuaki, Clasen, Lukas, Bejinariu, Alexandru, Klein, Kathrin, Makimoto, Asuka, Jung, Christian, Westenfeld, Ralf, Zeus, Tobias, Kelm, Malte
• Format: Journal Article
• Language: English
• Published: England 01.06.2022
• Subjects: Screening; Understandable AI; Auscultation; Artificial intelligence; Aortic valve stenosis
• ISSN: 2634-3916; 2634-3916
• DOI: 10.1093/ehjdh/ztac029

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. 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. Clinical validation was performed with the developed smartphone application in an independent cohort (model establishment:  = 556, clinical validation:  = 132). Our ensemble technique integrating the heart sounds from multiple auscultation locations increased the detection accuracy of CNN model by compensating detection errors. The established smartphone application achieved a sensitivity, specificity, accuracy, and F1 value of 97.6% (41/42), 94.4% (85/90), 95.7% (126/132), and 0.93, respectively, which were higher compared with the consensus of cardiologists (81.0%, 93.3%, 89.4%, and 0.829, respectively), implying a good utility for severe AS screening. The Gradient-based Class Activation Map demonstrated that the built AIs could focus on specific heart sounds to differentiate the severity of AS. Our CNN model combining multiple auscultation locations and exported on smartphone application could efficiently identify severe AS based on heart sounds. The visual explanation of AI decisions for heart sounds was interpretable. These technologies may support medical training and remote consultations.