Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial

• Published in: Digital health 2024-01, Vol.10, p.20552076241233689-20552076241233689
• Main Authors: Hirosawa, Takanobu, Sakamoto, Tetsu, Harada, Yukinori, Tokumasu, Kazuki, Shimizu, Taro
• Format: Article
• Language: English
• Subjects: Auscultation; Clinical decision making; Clinical trials; Decision support systems; Machine learning; Original Research
• ISSN: 2055-2076; 2055-2076
• DOI: 10.1177/20552076241233689

Background The utility of a clinical decision support system using a machine learning (ML) model for simultaneous cardiac and pulmonary auscultation is unknown. Objective This study aimed to develop and evaluate an ML system's utility for cardiopulmonary auscultation. Methods First, we developed an ML system for cardiopulmonary auscultation, using cardiopulmonary sound files from our previous study. The technique involved pre-processing, feature extraction, and classification through several neural network layers. After integration, the output class was categorized as “normal,” “abnormal,” or “undetermined.” Second, we evaluated the ML system with 24 junior residents in an open-label randomized controlled trial at a university hospital. Participants were randomly assigned to the ML system group (intervention) or conventional auscultation group (control). During training, participants listened to four cardiac and four pulmonary sounds, all of which were correctly classified. Then, participants classified a series of 16 simultaneous cardiopulmonary sounds. The control group auscultated the sounds using noise-cancelling headphones, while the intervention group did so by watching recommendations from the ML system. Results The total scores for correctly identified normal or abnormal cardiopulmonary sounds in the intervention group were significantly higher than those in the control group (366/384 [95.3%] vs. 343/384 [89.3%], P = 0.003). The cardiac test score in the intervention group was better (111/192 [57.8%] vs. 90/192 [46.9%], P = 0.04); there was no significant difference in pulmonary auscultation. Conclusions The ML-based system improved the accuracy of cardiopulmonary auscultation for junior residents. This result suggests that the system can assist early-career physicians in accurate screening.