Development of multi‐class computer‐aided diagnostic systems using the NICE/JNET classifications for colorectal lesions
Background and Aim Diagnostic support using artificial intelligence may contribute to the equalization of endoscopic diagnosis of colorectal lesions. We developed computer‐aided diagnosis (CADx) support system for diagnosing colorectal lesions using the NBI International Colorectal Endoscopic (NICE)...
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Published in | Journal of gastroenterology and hepatology Vol. 37; no. 1; pp. 104 - 110 |
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Main Authors | , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Australia
Wiley Subscription Services, Inc
01.01.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Background and Aim
Diagnostic support using artificial intelligence may contribute to the equalization of endoscopic diagnosis of colorectal lesions. We developed computer‐aided diagnosis (CADx) support system for diagnosing colorectal lesions using the NBI International Colorectal Endoscopic (NICE) classification and the Japan NBI Expert Team (JNET) classification.
Methods
Using Residual Network as the classifier and NBI images as training images, we developed a CADx based on the NICE classification (CADx‐N) and a CADx based on the JNET classification (CADx‐J). For validation, 480 non‐magnifying and magnifying NBI images were used for the CADx‐N and 320 magnifying NBI images were used for the CADx‐J. The diagnostic performance of the CADx‐N was evaluated using the magnification rate.
Results
The accuracy of the CADx‐N for Types 1, 2, and 3 was 97.5%, 91.2%, and 93.8%, respectively. The diagnostic performance for each magnification level was good (no statistically significant difference). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CADx‐J were 100%, 96.3%, 82.8%, 100%, and 96.9% for Type 1; 80.3%, 93.7%, 94.1%, 79.2%, and 86.3% for Type 2A; 80.4%, 84.7%, 46.8%, 96.3%, and 84.1% for Type 2B; and 62.5%, 99.6%, 96.8%, 93.8%, and 94.1% for Type 3, respectively.
Conclusions
The multi‐class CADx systems had good diagnostic performance with both the NICE and JNET classifications and may aid in educating non‐expert endoscopists and assist in diagnosing colorectal lesions. |
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Bibliography: | Yuki Okamoto, Shigeto Yoshida, Seiji Izakura, Daisuke Katayama, Ryuichi Michida, Tetsushi Koide, Toru Tamaki, Fumiaki Tanino, Noriko Yamamoto, Yuki Kamigaichi, Hirosato Tamari, Yasutsugu Shimohara, Tomoyuki Nishimura, Katsuaki Inagaki, Hidenori Tanaka, Ken Yamashita, Kyoku Sumimoto, Shiro Oka, and Shinji Tanaka have no conflicts of interest or financial ties to disclose. Declaration of conflict of interest ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0815-9319 1440-1746 |
DOI: | 10.1111/jgh.15682 |