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NONAKA Kouichi
Department School of Medicine(Tokyo Women's Medical University Hospital), School of Medicine Position Professor |
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| Article types | Original article |
| Language | English |
| Peer review | Non peer reviewed |
| Title | Comparison of Endoscopic and Artificial Intelligence Diagnoses for Predicting the Histological Healing of Ulcerative Colitis in a Real-World Clinical Setting |
| Journal | Formal name：Crohn's & colitis 360 Abbreviation：Crohns Colitis 360 ISSN code：2631827X/2631827X |
| Domestic / Foregin | Foregin |
| Volume, Issue, Page | 6(1),pp.otae005 |
| Author and coauthor | OMORI Teppei†, YAMAMOTO Tomoko, Murasugi Shun, KOROKU Miki, YONEZAWA Maria, NONAKA Kouichi, NAGASHIMA Yoji, NAKAMURA Shinichi, TOKUSHIGE Katsutoshi |
| Publication date | 2024/01 |
| Summary | BACKGROUND:Artificial intelligence (AI)-assisted colonoscopy systems with contact microscopy capabilities have been reported previously; however, no studies regarding the clinical use of a commercially available system in patients with ulcerative colitis (UC) have been reported. In this study, the diagnostic performance of an AI-assisted ultra-magnifying colonoscopy system for histological healing was compared with that of conventional light non-magnifying endoscopic evaluation in patients with UC.METHODS:The data of 52 patients with UC were retrospectively analyzed. The Mayo endoscopic score (MES) was determined by 3 endoscopists. Using the AI system, healing of the same spot assessed via MES was defined as a predicted Geboes score (GS) < 3.1. The GS was then determined using pathology specimens from the same site.RESULTS:A total of 191 sites were evaluated, including 159 with a GS < 3.1. The MES diagnosis identified 130 sites as MES0. A total of 120 sites were determined to have healed based on AI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MES0 for the diagnosis of GS < 3.1 were 79.2%, 90.6%, 97.7%, 46.8%, and 81.2%, respectively. The AI system performed similarly to MES for the diagnosis of GS < 3.1: sensitivity, 74.2%; specificity: 93.8%; PPV: 98.3%; NPV: 42.3%; and accuracy: 77.5%. The AI system also significantly identified a GS of < 3.1 in the setting of MES1 (P = .0169).CONCLUSIONS:The histological diagnostic yield the MES- and AI-assisted diagnoses was comparable. Healing decisions using AI may avoid the need for histological examinations. |
| DOI | 10.1093/crocol/otae005 |
| PMID | 38419859 |