Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale
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- Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy Keio University School of Medicine Tokyo Japan
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- Taku Kobayashi
- Center for Advanced IBD Research and Treatment Kitasato University Kitasato Institute Hospital Tokyo Japan
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- Katsuyoshi Matsuoka
- Division of Gastroenterology and Hepatology, Department of Internal Medicine Toho University Sakura Medical Center Chiba Japan
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- Barrett G. Levesque
- Division of Gastroenterology Los Angeles County/University of Southern California Medical Center Los Angeles USA
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- Takuji Kawamura
- Department of Gastroenterology Kyoto Second Red Cross Hospital Kyoto Japan
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- Kiyohito Tanaka
- Department of Gastroenterology Kyoto Second Red Cross Hospital Kyoto Japan
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- Takeaki Kadota
- Department of Advanced Information Technology Kyushu University Fukuoka Japan
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- Ryoma Bise
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
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- Seiichi Uchida
- Research Center for Medical Bigdata National Institute of Informatics Tokyo Japan
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- Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine Keio University School of Medicine Tokyo Japan
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- Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy Keio University School of Medicine Tokyo Japan
説明
<jats:sec><jats:title>Objectives</jats:title><jats:p>Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. However, inflammatory bowel disease (IBD) expert endoscopists categorize the severity and diagnose the overall impression of the degree of inflammation. This study aimed to develop an artificial intelligence (AI) system that can accurately represent the assessment of the endoscopic severity of UC by IBD expert endoscopists.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>A ranking‐convolutional neural network (ranking‐CNN) was trained using comparative information on the UC severity of 13,826 pairs of endoscopic images created by IBD expert endoscopists. Using the trained ranking‐CNN, the UC Endoscopic Gradation Scale (UCEGS) was used to express severity. Correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using UCEGS diagnosed by the AI and the Mayo Endoscopic Subscore, and the correlation coefficients of the mean for test images assessed using UCEGS by four IBD expert endoscopists and the AI.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Spearman's correlation coefficient between the UCEGS diagnosed by AI and Mayo Endoscopic Subscore was approximately 0.89. The correlation coefficients between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (<jats:italic>P</jats:italic> < 0.01).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The AI developed here can diagnose UC severity endoscopically similar to IBD expert endoscopists.</jats:p></jats:sec>
収録刊行物
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- Digestive Endoscopy
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Digestive Endoscopy 36 (5), 582-590, 2023-10-11
Wiley
