1. A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study.
- Author
-
Horimasu Y, Ohshimo S, Yamaguchi K, Sakamoto S, Masuda T, Nakashima T, Miyamoto S, Iwamoto H, Fujitaka K, Hamada H, Sadamori T, Shime N, and Hattori N
- Subjects
- Aged, Aged, 80 and over, Algorithms, Auscultation methods, Disease Progression, Female, Humans, Idiopathic Pulmonary Fibrosis diagnosis, Idiopathic Pulmonary Fibrosis epidemiology, Lung Diseases, Interstitial diagnostic imaging, Male, Middle Aged, Radiography, Thoracic methods, Tomography, X-Ray Computed methods, Auscultation instrumentation, Lung diagnostic imaging, Lung Diseases, Interstitial diagnosis, Machine Learning statistics & numerical data, Respiratory Sounds diagnosis
- Abstract
Abstract: Fine crackles are frequently heard in patients with interstitial lung diseases (ILDs) and are known as the sensitive indicator for ILDs, although the objective method for analyzing respiratory sounds including fine crackles is not clinically available. We have previously developed a machine-learning-based algorithm which can promptly analyze and quantify the respiratory sounds including fine crackles. In the present proof-of-concept study, we assessed the usefulness of fine crackles quantified by this algorithm in the diagnosis of ILDs.We evaluated the fine crackles quantitative values (FCQVs) in 60 participants who underwent high-resolution computed tomography (HRCT) and chest X-ray in our hospital. Right and left lung fields were evaluated separately.In sixty-seven lung fields with ILDs in HRCT, the mean FCQVs (0.121 ± 0.090) were significantly higher than those in the lung fields without ILDs (0.032 ± 0.023, P < .001). Among those with ILDs in HRCT, the mean FCQVs were significantly higher in those with idiopathic pulmonary fibrosis than in those with other types of ILDs (P = .002). In addition, the increased mean FCQV was associated with the presence of traction bronchiectasis (P = .003) and honeycombing (P = .004) in HRCT. Furthermore, in discriminating ILDs in HRCT, an FCQV-based determination of the presence or absence of fine crackles indicated a higher sensitivity compared to a chest X-ray-based determination of the presence or absence of ILDs.We herein report that the machine-learning-based quantification of fine crackles can predict the HRCT findings of lung fibrosis and can support the prompt and sensitive diagnosis of ILDs., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)
- Published
- 2021
- Full Text
- View/download PDF