Content-based image retrieval using deep learning for interstitial lung disease diagnosis by chest CT

Choe, Jooae; Hwang, Hye Jeon; Seo, Joon Beom; Lee, Sang Min; Yun, Jihy; Kim, Minju; Jeong, Jewon ; Lee, Youngsoo; Jin, Kiok; Rohee, Park; Jihoon, Kim; Jeon, Howook; Kim, Namkug; Yi, Jaeyoun; Yu, Donghoon; Kim, Byeongsoo Kim
Radiology, 2021
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Date Published

This retrospective study evaluates the impact of a content-based image retrieval (CBIR) system utilizing deep learning on the diagnostic accuracy of interstitial lung disease (ILD) in chest CT images. The system, employing Coreline Soft's AVIEW Lung Texture algorithm, was tested on 288 patients with confirmed ILD and four different disease classes. The diagnostic process involved eight readers of varying experience levels, and results were compared before and after the application of CBIR. Findings revealed a significant improvement in overall diagnostic accuracy (from 46.1% to 60.9%), particularly in cases of usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP). Moreover, interreader agreement also improved markedly. The study concludes that the deep learning-enhanced CBIR system notably enhances diagnostic accuracy and interreader consensus in ILD cases, highlighting the potential utility of AVIEW software for future commercialization.


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