A116 OPTIMIZING CROHN’S DISEASE ENDOSCOPIC SCORING OPERATING CHARACTERISTICS TO ASSIST WITH THE ADVENT OF ARTIFICIAL INTELLIGENCE
Bibliographic record
Abstract
Abstract Background To date, no studies have used Artificial Intelligence (AI) to produce standard endoscopic scores for Crohn’s Disease (CD). These scores are essential to clinical practice and have a major cost in clinical trials. Purpose We aimed to re-examine the components of the Simple Endoscopic Score for Crohn’s Disease (SES-CD) and Modified Multiplier (MM-SES-CD) to see if scoring of individual colonic segments could be combined while maintaining predictive value accuracy, thus simplifying scoring for AI. Method Data from 279 participants in the UNITI and EXTEND trials as well as an infliximab biosimilar trial (NCT02096861) were used in this retrospective study. The primary outcome was endoscopic remission (ER-1) of the colon, defined as absence of ulcers. Secondary outcomes included alternative definitions of colonic endoscopic remission including SES-CD score of 0 (ER-2), SES-CD <3 (ER-3), and SES-CD reduction by 50% or greater from baseline (colonic endoscopic response). Result(s) The mean baseline SES-CD score was 13.7 (SD 7.7) and the mean MM-SES-CD score was 22.8 (SD 12.4). Among all possible colonic segment combinations, the combination of Right + Rectum + worst of Left or Transverse performed best for prediction of one-year ER-1, with fair accuracy (AUC 0.71, 95% CI 0.65-0.78, p < 0.0001). Accuracy of prediction of one-year ER-2 was poor (AUC 0.68, 95% CI 0.62-0.75, p < 0.0001), ER-3 was fair (AUC 0.71, 95% CI 0.65-0.77, p < 0.0001), and prediction of endoscopic response was poor (AUC 0.64, 95% CI 0.56-0.72, p > 0.01). These results were very similar to conventional scoring (without combining any “worst of” segments) with all of the colonic MM-SES-CD scores, with AUCs of 0.70, 0.69, 0.71, and 0.61 for the respective outcomes. Assessing only for the presence of ulcers was as accurate as the full assessment and exclusion of scoring stenosis had minimal impact on scores. Image Conclusion(s) By combining assessment of colonic segments accuracy was maintained compared with currently used endoscopic scoring. Assessing for features other than ulceration had no significant impact on overall scores. By combining colonic segments and limiting assessment to ulceration, it may be simpler to develop AI algorithms reliably scoring endoscopic severity of CD for clinical practice and trials. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".