Development of a CT image case database and content-based image retrieval system for non-cancerous respiratory diseases: Method and preliminary assessment

• Published in: Respiratory Investigation Vol. 57; no. 5; pp. 490 - 498
• Main Authors: Oosawa, Akira, Kurosaki, Atsuko, Kanada, Shoji, Takahashi, Yui, Ogawa, Kazumasa, Hanada, Shigeo, Uruga, Hironori, Takaya, Hisashi, Morokawa, Nasa, Kishi, Kazuma
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2019; Elsevier BV
• Subjects: Case database; Chest computed tomography; Content-based image retrieval system; Data Management; Data Management - methods; Databases, Factual; Diagnosis, Differential; Humans; Image Interpretation, Computer-Assisted; Information Systems; Non-cancerous lung diseases; Respiratory Tract Diseases; Respiratory Tract Diseases - diagnostic imaging; Tomography, X-Ray Computed; Tomography, X-Ray Computed - methods; ILD; MP; Content-based image retrieval system; COP; NCRDs; HP; Case database; TB; CEP; Chest computed tomography; CBIR; DPB; LAM; HRCT; Non-cancerous lung diseases; PCP; DB; IPF
• ISSN: 2212-5345; 2212-5353; 2212-5353
• DOI: 10.1016/j.resinv.2019.03.015

To evaluate the benefits of using a CT image case database (DB) with content-based image retrieval system for the diagnosis of typical non-cancerous respiratory diseases. Using this DB, which comprised data on 191 cases covering 69 diseases, 933 imaging findings that contributed to differential diagnoses were annotated. Ten test cases were selected. Image similarity between each marked test case lesion and the lesions of the top 10 retrieved cases were assessed and classified as similar, somewhat similar, or dissimilar by two physicians in consensus. Additionally, the accuracy of five internal medicine residents’ abilities to interpret CT findings and provide disease diagnoses with and without the proposed system was evaluated by image interpretation experiments involving five test cases. The rates of concordance between the subjects’ interpretations and the correct answers prepared in advance by two specialists in consensus were converted into scores. The mean (± SD) of image similarity among the 10 test cases was as follows: 5.1 ± 2.7 (similar), 2.9 ± 1.0 (somewhat similar), and 2.0 ± 2.4 (dissimilar). Using the proposed system, the subjects’ mean score for the correct interpretation of CT findings improved from 15.1 to 28.2 points (p = 0.131) and for the correct disease diagnoses, from 9.3 to 28.2 points (p = 0.034). Although this was a preliminary small-scale assessment, the results suggest that this system may contribute to an improved interpretation of CT findings and differential diagnosis of non-cancerous respiratory diseases, which are difficult to diagnose for inexperienced physicians.