Results of the Ninth Scientific Workshop of the European Crohn’s and Colitis Organisation (ECCO): artificial intelligence in endoscopy, radiology, and histology in inflammatory bowel disease diagnostics
Bibliographic record
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 the 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 harmonizing 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 the pathophysiology of IBD. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".