P777 Deployment of an artificial intelligence tool for precision medicine in ulcerative colitis: Preliminary data from 8 globally distributed clinical sites. (30th January 2023)

• Record Type: Journal Article
• Authors: Peyrin-Biroulet, L; Rubin, D; Weber, C; Adsul, S; Freire, M; Biedermann, L; Koelzer, V; Bressler, B; Xiong, W; Niess, J; Matthias Matter, M M; Kopylov, U; Barshack, I; Mayer, C; Magro, F; Carneiro, F; Maharshak, N; Greenberg, A; Hart, S; Dehmeshki, J; Kubassova, O
• Is Part Of: Journal of Crohn's and colitis. Volume 17(2023)Supplement 1
• Page Start: i910
• Page End: i911
• Publication Date: 2023-01-30
• Subjects: Inflammatory bowel diseases -- Periodicals; 616.344005
• DOI: 10.1093/ecco-jcc/jjac190.0907
• Languages: English
• ISSNs: 1873-9946

Abstract

Abstract: Background: Histological remission is an important target for Ulcerative Colitis (UC) treatment; however, scoring of histological images is time-consuming and prone to inter and intra-observer variability. Thus, a need exists for an accurate, reproducible, and reliable automated method. Previously, we demonstrated an Artificial Intelligence (AI) Tool using image processing and machine learning algorithms to measure histological disease activity using the Nancy index consistently and accurately. 1 Here, we aim to enhance the capabilities of the AI Tool, by adding substantially more population-diversified training data while maintaining accuracy and robustness of results. Methods: Eight global sites submitted 600 UC histological images. These were added to the 200 images previously used to train and validate the AI Tool. The 800-image dataset was divided into 2 groups: 90% used for training, 10% for testing. The novel AI algorithms were trained using state-of-the-art image processing and machine learning techniques based on deep learning and feature extraction. Cell and tissue regions of each training image were manually annotated, measured, and assigned a Nancy Index independently by 3 histopathologists, and used to further train the AI using over 43, 000 characterisations. The AI Tool fully characterises histological images, identifying tissue types, cell types, cell numbers and locations, and automatically measures the Nancy Index for each image. Intra Class Correlation (ICC) and Confusion Matrix analyses were performed to evaluate the AI Tool and assess accuracy. Results: The average ICC was 92.1% among the histopathologists and 91.1% between histopathologists and AI Tool, compared with 88.3% and 87.2% in the previous study. 1 Confusion matrix analysis (Table 1) demonstrated the strongest correlation at the extremes of the Nancy Index, with 80% correlation between predicted and true labels for Nancy Scores of 0 or 4. When 2 adjacent scores were combined, correlations were stronger: 96% for a true Nancy score of 0 being predicted as 0 or 1, and 100% for a true Nancy score of 2 being predicted as 2 or 3. Conclusion: By adding a larger number of images to the AI Tool training data, the robustness of the AI Tool was substantially improved while maintaining accuracy. The continued high correlation of AI Tool performance with the histopathologists reinforces the potential role for the AI Tool for IBD clinical applications. Fully characterising whole slides could standardise and validate an AI-driven scoring system for histology slides in IBD, eliminating the subjectivity of the human pathologist in assessment of disease activity. References: 1. Peyrin-Biroulet L, et al. J Crohn's and Colitis . 2022;16(Suppl 1):i105.