Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: A cohort study. (January 2022)

• Record Type: Journal Article
• Authors: Zhu, Hongling; Lai, Jinsheng; Liu, Bingqiang; Wen, Ziyuan; Xiong, Yulong; Li, Honglin; Zhou, Yuhua; Fu, Qiuyun; Yu, Guoyi; Yan, Xiaoxiang; Yang, Xiaoyun; Zhang, Jianmin; Wang, Chao; Zeng, Hesong
• Is Part Of: Computer methods and programs in biomedicine. Volume 213(2022)
• Publication Date: 2022-01
• Subjects: Auscultation -- Coronavirus Disease 2019 (COVID-19) -- convolutional neural network (CNN) -- deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106500
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A first study to investigate the auscultation characteristics of covid-19. To grading diagnose covid-19 based on auscultation with deep learning algorithms. Three CNN models in classifying auscultation of covid-19 achieve superior performance. Unidimensional raw data prevents complex pre-processing & manual feature extraction. Abstracts: Background and Objective: Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. Methods: 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. Results: There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923–0.9952), AUC ROC score (0.9999 95% CI 0.9998–1.0000), sensitivity (0.9938 95% CI 0.9910–0.9965) and specificity (0.9979 95% CI 0.9970–0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440–0.9508), AUC ROC score (0.9762 95% CI 0.9848–0.9865), sensitivity (0.9482 95% CI 0.9393–0.9578) and specificity (0.9835 95% CI 0.9806–0.9863). Conclusions: Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.