DOP14 Validation of a new OPtical diagnosis Training platform to Improve dysplasia Characterisation in Inflammatory Bowel Disease (OPTIC-IBD): A multicentre randomised controlled study
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD) increases colorectal cancer risk. To mitigate this patients undergo endoscopic surveillance to detect dysplasia. However, chronic inflammation alters mucosal and vascular colonic architecture, complicating lesion recognition. Endoscopic advances enhance our ability to accurately characterise these lesions. But training on optical diagnosis of dysplastic lesions in IBD is not widely available. We aim to fill this gap by developing and validating the new OPTIC-IBD online training platform (Figure 1, NCT04924543, funding GutsUK TRN2019-03). Methods We designed an interactive, self-directed, multi-modality learning module. This includes surveillance principles, optical diagnosis methods, characterisation approach, classifications (SCENIC, Kudo, FACILE1), examples and self-assessments. We invited participants from Canada, Italy and the UK, including novice (<100 lifetime colonoscopies), intermediate and experienced endoscopists (≥1000). Assessments comprised 24 short endoscopic videos of IBD colonic lesions, divided into 8 non-dysplastic (hyperplastic, inflammatory, sessile serrated lesion [SSL]) and 16 dysplastic lesions (SSL-D, low grade and high grade dysplasia, cancer). Participants classified lesions, predicted histology and rated their confidence. All participants completed online training and feedback. The videos were repeated in a random order after ≥7 days. Participants were then randomised 1:1 to get feedback and extra training. All had a final assessment at 60 days with prior/new videos and similar case mix. We report diagnostic performance for dysplasia, interrater reliability and rater confidence. Results We present a planned interim analysis of 77 participants after pre- and post-course assessments (Table 1). Diagnostic accuracy improved (primary endpoint: 44.5 to 54.0%, P<0.0001), particularly for novice and intermediate endoscopists. Sensitivity for dysplasia increased (50.3 to 59.1%) in line with prior experience. Specificity and accuracy were most improved for high confidence diagnoses (44.9 to 70.3% and 55.0 to 64.6%). In multilevel logistic regression, training was associated with correct diagnoses for high confidence (OR 1.40, 1.13–1.77) but not low confidence ratings (OR 1.09, 0.96–1.25). Training improved precision between participants (Table 2) and their confidence (Table 3). Conclusion The OPTIC-IBD training module improved participants’ accuracy, precision and confidence in optical diagnosis of dysplasia. Next, we will study the training approaches and classification systems that can best be adopted by non-experts and trainees. Our refined training platform will be made available to improve quality of endoscopic care for people with IBD. Reference 1. Iacucci et al Endoscopy 2019;51(2):133
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".