Medicine and Health

The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence

C. Huang, C. Chen, et al.

This prospective study reveals the reliability challenges of identifying crackles and wheezes in breath sounds, conducted by Chun-Hsiang Huang, Chi-Hsin Chen, Jing-Tong Tzeng, An-Yan Chang, Cheng-Yi Fan, Chih-Wei Sung, Chi-Chun Lee, and Edward Pei-Chuan Huang. While both physicians and AI performed well for wheezing, crackles proved less reliable, raising important questions for medical decision-making. Discover the intricacies of this fascinating research!

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Abstract

This prospective study investigated the reliability of identifying crackles and wheezes in breath sounds using both physicians and artificial intelligence (AI). 11,532 breath sound files were labeled by five physicians and six AI models. Doubtful labels were re-evaluated by two additional physicians. While both physicians and AI showed good sensitivity and specificity for wheezing, crackles demonstrated good sensitivity but poor specificity, indicating their unreliability for medical decision-making. Further investigation into the challenges of crackle identification is warranted.

Publisher

npj Primary Care Respiratory Medicine

Published On

Oct 15, 2024

Authors

Chun-Hsiang Huang, Chi-Hsin Chen, Jing-Tong Tzeng, An-Yan Chang, Cheng-Yi Fan, Chih-Wei Sung, Chi-Chun Lee, Edward Pei-Chuan Huang

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