Results of the 9th Scientific Workshop of the European Crohn's and Colitis Organisation (ECCO): Artificial Intelligence in Endoscopy, Radiology and Histology in IBD Diagnostics.

Abstract

In this review, a comprehensive overview of the current state of artificial intelligence (AI) research in Inflammatory Bowel Disease (IBD) diagnostics in the domains of endoscopy, radiology and histology is presented. Moreover, key considerations for development of AI algorithms in medical image analysis are discussed. AI presents a potential breakthrough in real-time, objective and rapid endoscopic assessment, with implications for predicting disease progression. It is anticipated that, by harmonising multimodal data, AI will transform patient care through early diagnosis, accurate patient profiling and therapeutic response prediction. The ability of AI in cross-sectional medical imaging to improve diagnostic accuracy, automate and enable objective assessment of disease activity and predict clinical outcomes highlights its transformative potential. AI models have consistently outperformed traditional methods of image interpretation, particularly in complex areas such as differentiating IBD subtypes, identifying disease progression and complications. The use of AI in histology is a particularly dynamic research field. Implementation of AI algorithms in clinical practice is still lagging, a major hurdle being the lack of a digital workflow in many pathology institutes. Adoption is likely to start with implementation of automatic disease activity scoring. Beyond matching pathologist performance, algorithms may teach us more about IBD pathophysiology. While AI is set to substantially advance IBD diagnostics, various challenges such as heterogeneous datasets, retrospective designs and assessment of different endpoints must be addressed. Implementation of novel standards of reporting may drive an increase in research quality and overcome these obstacles.