Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis

Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis

We investigated the feasibility of a new deep-learning (DL)-based lung analysis method for the evaluation of interstitial lung disease (ILD) by comparing it with evaluation using the traditional computer-aided diagnosis (CAD) system and patients’ clinical outcomes. We prospectively included 104 pati...

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Bibliographic Details
Main Authors: Ryo Aoki, Tae Iwasawa, Tomoki Saka, Tsuneo Yamashiro, Daisuke Utsunomiya, Toshihiro Misumi, Tomohisa Baba, Takashi Ogura
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Diagnostics
Subjects:
computed tomography
deep-learning
interstitial lung disease
quantitative analyses
Online Access:https://www.mdpi.com/2075-4418/12/12/3038

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