Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children

• Published in: Frontiers in pediatrics 2021-03, Vol.9, p.627337-627337
• Main Authors: Zhang, Jing, Wang, Han-Song, Zhou, Hong-Yuan, Dong, Bin, Zhang, Lei, Zhang, Fen, Liu, Shi-Jian, Wu, Yu-Fen, Yuan, Shu-Hua, Tang, Ming-Yu, Dong, Wen-Fang, Lin, Jie, Chen, Ming, Tong, Xing, Zhao, Lie-Bin, Yin, Yong
• Format: Article
• Language: English
• Subjects: artificial intelligence; auscultation; breath sound; children; electronic stethoscope; Pediatrics
• ISSN: 2296-2360; 2296-2360
• DOI: 10.3389/fped.2021.627337

Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases. The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated. A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% ( < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, < 0.001) among the age groups. In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.