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.
Authors
Affiliations (21)
Affiliations (21)
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
- Department of Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium.
- IRCCS Hospital San Raffaele and University Vita-Salute San Raffaele, Gastroenterology and Endoscopy, Milan, Italy.
- Department of Gastroenterology, Sheba Medical Center, Ramat Gan, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- Department of Pathology, Ankara University Medical School, Ankara, Turkey.
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
- Division of Gastroenterology and Hepatology, University of Alberta, Edmonton, Alberta, Canada.
- Department of Gastroenterology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
- OpenSourceResearch organisation, Aalborg, Denmark.
- St Marks Hospital, London, United Kingdom.
- Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Derio, Basque Country, and Ikerbasque, Basque Foundation for Science, Integrative Genomics Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Bilbao, Bizkaia, 48013, Spain.
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
- Department of Gastroenterology, Imelda General Hospital, Bonheiden, Belgium.
- Department of Surgery, University of Campania "Luigi Vanvitelli," Naples, Italy.
- Department of Internal Medicine, Arcispedale Santa Maria Nuova, IRCCS, Reggio Emilia, Italy.
- University Giessen, Giessen, Germany.
- Gastroenterology Unit, AO Ordine Mauriziano, Turin, Italy.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- IBD unit, Department of Gastroenterology, Hull University Teaching Hospitals NHS Trust, Hull, UK.
- Hull York Medical School, Hull, UK.
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.