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
Department of Respiratory Medicine, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Paediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, Shanghai, China.
Front Pediatr. 2021 Mar 23;9:627337. doi: 10.3389/fped.2021.627337. eCollection 2021.
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.
肺部听诊在儿童肺部疾病的诊断中起着重要作用。本研究的目的是评估一种人工智能（AI）算法在真实临床环境中对患有肺部疾病儿童呼吸音的检测应用。使用电子听诊器在上海儿童医学中心（SCMC）呼吸科收集呼吸音。记录了所有胸部位置相对于由SCMC的2名经验丰富的儿科肺病专家和6名普通儿科医生建立的金标准（GS）的判别结果。评估了AI算法和普通儿科医生相对于GS的准确性、敏感性、特异性、精确性和F1分数。同时，评估了AI算法在不同患者年龄和记录位置的性能。2019年5月至12月共招募了112名患有肺部疾病的住院儿童参与该研究。共收集了672个呼吸音，其中627个（93.3%）呼吸音，包括159个湿啰音（23.1%）、264个哮鸣音（38.4%）和264个正常呼吸音（38.4%），由AI算法进行了全面分析。AI算法和普通儿科医生相对于GS检测附加呼吸音的准确性分别为77.7%和59.9%（<0.001）。AI算法在检测湿啰音和哮鸣音方面的敏感性、特异性和F1分数高于普通儿科医生（湿啰音分别为81.1%对47.8%、94.1%对77.1%、80.9%对42.74%；哮鸣音分别为86.4%对82.2%、83.0%对72.1%、80.9%对72.5%；<0.001）。性能因患者年龄而异，12个月以下的患者在各年龄组中准确性最高（81.3%，<0.001）。在真实临床环境中，通过电子听诊器远程收集儿童呼吸音；这些呼吸音可被儿科医生和AI算法识别。AI算法分析附加呼吸音的能力优于普通儿科医生。
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