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Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT.
Jooae Choe1, Hye Jeon Hwang1, Joon Beom Seo1
1From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.).
Deep learning-powered image retrieval significantly improved the diagnostic accuracy for interstitial lung disease (ILD) and reduced variability among radiologists. This AI tool enhances the interpretation of chest CT scans for conditions like usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP).
Area of Science:
- Radiology
- Artificial Intelligence
- Medical Imaging
Background:
- Diagnosing interstitial lung disease (ILD) on CT scans is challenging due to required expertise and significant interreader variability.
- Content-based image retrieval (CBIR) using deep learning offers a potential solution to aid in ILD diagnosis.
Purpose of the Study:
- To evaluate if a deep learning-based CBIR system can improve the diagnostic accuracy of ILD in chest CT images.
- To assess the impact of CBIR on radiologists with varying experience levels.
Main Methods:
- A retrospective study using a database of 288 patients with confirmed ILD (UIP, NSIP, cryptogenic organizing pneumonia, chronic hypersensitivity pneumonitis).
- A deep learning algorithm quantified disease patterns to retrieve similar chest CT images.
- Eight readers interpreted CT images before and after using the CBIR system, with diagnostic accuracy and interreader agreement analyzed.
Main Results:
- Overall diagnostic accuracy improved from 46.1% before CBIR to 60.9% after CBIR (P < .001).
- Diagnostic accuracy specifically improved for usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP).
- Interreader agreement increased from a Fleiss κ of 0.32 to 0.47 after implementing CBIR (P = .005).
Conclusions:
- The proposed CBIR system utilizing deep learning enhances diagnostic accuracy for ILD.
- The system improves diagnostic consistency among radiologists with different experience levels.
- CBIR shows promise in supporting the accurate and reliable diagnosis of interstitial lung diseases on CT.
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