What question did this study set out to answer?

The aim is to develop an AI-driven system for objective assessment of intestinal barrier impairment in IBD.

January 23, 2026

P0428AI-Driven automated assessment of intestinal barrier impairment in IBD using probe confocal laser endomicroscopy

Key Points

The aim is to develop an AI-driven system for objective assessment of intestinal barrier impairment in IBD.
Developed an AI algorithm using 156 high-quality pCLE videos from IBD patients.
Endoscopists assessed barrier impairment with a pCLE-scoring system as a reference standard.
Utilized weakly supervised learning and frame-level feature extraction to generate video-level scores.
Evaluated diagnostic performance through sensitivity, specificity, and accuracy metrics.
76% of videos showed barrier impairment, with higher rates in the UK cohort.
AI model achieved 77% sensitivity and 75% specificity in detecting overall barrier impairment.
Detected epithelial and vascular impairments with varying sensitivities (70% and 85%) and specificities (72% and 77%).
Diagnostic performance was better for ulcerative colitis than Crohn's disease.

Abstract

Abstract Background Barrier healing is emerging as a key therapeutic target in Inflammatory bowel disease (IBD), being more closely associated with sustained remission and improved long-term outcomes than endoscopic and histological remission alone. Accurate assessment of barrier integrity is therefore crucial to optimise disease management. Probe-based confocal laser endomicroscopy (pCLE) enables real-time structural and functional assessment of intestinal barrier. However, its interpretation remains complex and highly operator-dependent, limiting its reproducibility and widespread application. Preliminary studies have demonstrated the feasibility of using automated assessment of specific pCLE features to predict therapeutic response1. We aimed to develop and validate an artificial intelligence (AI)-driven pCLE system for standardised, objective assessment of barrier impairment (BI) in IBD. Methods One hundred fifty-six high-quality pCLE videos from IBD patients undergoing colonoscopy in Ireland and UK were considered. Experienced endoscopists assessed the presence of BI according to a previously developed pCLE-scoring system2, providing a reference standard for AI. An AI algorithm was developed to predict pCLE-based BI using a weakly supervised learning paradigm that exclusively employs video-level scoring. Frame-level features were extracted with ViT pretrained under DINOv2 self-supervised learning framework3 and transformer-based architecture4 aggregated these into a global video representation. A linear layer classifier with two output neurons generated the final video-level BI score. A total of 105 videos were used for training and validation of the framework, while 51 were reserved for testing. The model’s ability to predict overall, epithelial, and vascular BI was evaluated. Diagnostic performance was expressed as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Results Overall, 48 IBD patients (28 CD and 20 UC) were considered. 119/156 (76%) videos showed BI, 68/101 (67%) in Ireland and 51/55 (93%) in the UK cohort. Table 1 details the diagnostic performance of the model. The AI algorithm achieved 77% sensitivity, 75% specificity, and 77% accuracy in detecting overall BI at the video level. Moreover, the model assessed the presence of epithelial and vascular BI with 70% and 85% sensitivity, 72% and 77% specificity, 71% and 82% accuracy. Interestingly, diagnostic performance was higher in UC than in CD. Conclusion Our AI model enables automated and standardised assessment of intestinal impairment in IBD, offering an objective and reproducible tool to monitor barrier healing as a novel therapeutic endpoint. References: 1. Iacucci M, Jeffery L, Acharjee A, et al. Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study. Inflamm Bowel Dis. 2023;29(9):1409-1420. doi:10.1093/ibd/izac233 2. Iacucci M, Majumder S, Zammarchi I, et al. Automated real-time imaging of intestinal barrier integrity and molecular profiling for early outcome prediction in inflammatory bowel disease - Endo-Histo-Barrier-Omics study. J Crohns Colitis. 3. Oquab M, Darcet T, Moutakanni T, et al. DINOv2: Learning Robust Visual Features without Supervision. Published online February 2, 2024. 4. Shao Z, Bian H, Chen Y, et al. TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification. Published online October 31, 2021. Conflict of interest: Iacucci, Marietta: No conflict of interest Dr. Zammarchi, Irene: No conflict of interest Carretero, Ilan: No conflict of interest Meseguer, Pablo: No conflict of interest Pugliano, Cecilia Lina: No conflict of interest Santacroce, Giovanni: No conflict of interest Del Amor, Rocio: No conflict of interest Chaudhuri, Ujwala: No conflict of interest Kolawole, Bisi Bode: No conflict of interest Crotty, Rory: No conflict of interest Hayes, Brian: No conflict of interest Ghosh, Subrata: None None None Grisan, Enrico: No conflict of interest Naranjo, Valery: No conflict of interest

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Cite This Study

Iacucci et al. (Thu,) studied this question.

synapsesocial.com/papers/69730f9fc8125b09b0d1f651 https://doi.org/https://doi.org/10.1093/ecco-jcc/jjaf231.609

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