P0472 Artificial intelligence-enabled histology is comparable in performance to pathologists in assessing histological remission in inflammatory bowel disease: a systematic review, meta-analysis and meta-regression
Authors: Puga Tejada M A , Majumder S , Maeda Y , Zammarchi I , Ditonno I , Santacroce G , Capobianco I , Robles-Medranda C , Ghosh S , Iacucci M 
Published: January 2025

Background

Achieving histological remission is a desirable treatment target in inflammatory bowel disease (IBD)1, yet its assessment is challenged by high inter- and intra-observer variability, reliance on experts, and lack of standardisation2. Artificial intelligence (AI) holds promise in addressing these issues3. This systematic review, meta-analysis, and meta-regression evaluated the AI’s performance in assessing histological remission and compared it with that of pathologists.

Methods

We searched Medline/PubMed and Scopus databases from inception to September 2024. We included studies on AI models assessing histological activity in IBD, with or without comparison to pathologists. Pooled performance metrics were calculated: sensitivity, specificity, positive and negative predictive value (PPV & NPV), observed agreement, and F1 score. A pairwise meta-analysis compared AI and pathologists, while sub-meta-analysis and meta-regression evaluated heterogeneity and factors influencing AI performance.

Results

From 432 studies retrieved, fourteen met the inclusion criteria. AI models exhibited strong performance with a pooled 0.85 sensitivity (95% CI 0.81-0.89), 0.88 specificity (0.85-0.91), 0.88 PPV (0.86-0.91), 0.83 NPV (0.76-0.89), 0.86 observed agreement (0.83-0.89), and 0.85 F1 score (0.82-0.88). AI demonstrated no significant differences when compared to pathologists’ PPV, observed agreement, and F1 score, but significantly lower sensitivity (OR 0.50, 95% CI 0.28-0.87; p=0.01) despite considerable heterogeneity (I2=0-74.6%). AI models for the adult population were linked to reduced heterogeneity and enhanced AI performance at meta-regression.

Conclusion

AI shows significant potential for assessing histological remission in IBD and performs comparably to pathologists. Future research should focus on standardised, large-scale studies to minimise heterogeneity and support widespread AI implementation in clinical practice.

References

1.Turner D, Ricciuto A, Lewis A, et al. STRIDE-II: An Update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): Determining Therapeutic Goals for Treat-to-Target strategies in IBD. Gastroenterology. 2021;160(5):1570-1583. doi:10.1053/j.gastro.2020.12.031

2.Gupta A, Yu A, Peyrin-Biroulet L, Ananthakrishnan AN. Treat to Target: The Role of Histologic Healing in Inflammatory Bowel Diseases: A Systematic Review and Meta-analysis. Clinical Gastroenterology and Hepatology. 2021;19(9):1800-1813.e4. doi:10.1016/j.cgh.2020.09.046

3.Iacucci M, Santacroce G, Zammarchi I, et al. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol. Published online May 2024. doi:10.1016/S2468-1253(24)00053-0Iacucci M, Santacroce G, Zammarchi I, et al. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol. Published online May 2024. doi:10.1016/S2468-1253(24)00053-0