Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT
| Autor: | Min Ju Kim, Sang Min Lee, Howook Jeon, Rohee Park, Hye Jeon Hwang, Ji-Hoon Kim, Kiok Jin, Youngsoo Lee, Byeongsoo Kim, Jooae Choe, Jaeyoun Yi, Namkug Kim, Donghoon Yu, Joon Beom Seo, Jewon Jeong, Jihye Yun |
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| Rok vydání: | 2021 |
| Předmět: |
Male
medicine.medical_specialty ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Chest ct Content-based image retrieval Diagnosis Differential Deep Learning Medicine Humans Radiology Nuclear Medicine and imaging Experience level Image retrieval Lung Retrospective Studies business.industry Deep learning Interstitial lung disease Reproducibility of Results Middle Aged medicine.disease Clinical Practice ComputingMethodologies_PATTERNRECOGNITION Radiographic Image Interpretation Computer-Assisted Female Artificial intelligence Radiology business Lung Diseases Interstitial Tomography X-Ray Computed |
| Zdroj: | Radiology. 302(1) |
| ISSN: | 1527-1315 |
| Popis: | Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3] |
| Databáze: | OpenAIRE |
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