Introduction

UC-SCALE[1] is an AI algorithm designed to assess endoscopic severity in ulcerative colitis (UC). It was trained on a large subset of Phase III Etrolizumab (ETRO) clinical trial data from 471 sites. In our previous work, UC-SCALE previously demonstrated excellent agreement (quadratic-weighted kappa >0.80) with centrally read Mayo Clinic Endoscopic Subscores (MCES) on an ETRO test set spanning 83 sites. While clinical trial data is highly controlled, its generalizability to real-world settings remained untested. Here, we assess UC-SCALE on a retrospective cohort from the Paris IBD Center, which we refer to as the Real World Dataset (RWD).

Aims & Methods

The RWD contained 276 unique UC patients (mean 1.17 visits/patient) from the Paris IBD center. For each colonoscopy video, two or three local gastroenterologists reviewed the recordings and reached a consensus MCES (0–3) per colonic segment (descending, sigmoid, rectum). Using the same, unmodified UC-SCALE model trained on ETRO, we evaluated both the RWD and ETRO test sets with the identical assessment protocol detailed in [1]. Quadratic-Weighted Kappa (QWK) quantified the agreement between ordinal predictions made by UC-SCALE and human scoring. Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the discriminative ability for each MCES class.

Results

On these real-world data, UC-SCALE’s section-level predictions closely mirrored local expert scoring. QWK reached ~0.804 - virtually identical to ETRO (~0.802). For class-specific discrimination (MCES 0–3), AUC values exceeded 0.85 in all classes and in several cases surpassed ETRO’s results. The average AUC across all classes approached 0.90 in the RWD, aligning well with performance in trial conditions. As in ETRO, performance was strongest for Mayo 0 and 3, but notably, predictions for intermediate scores (Mayo 1 and 2) were even more accurate in RWD. These results highlight UC-SCALE’s robust generalizability to routine clinical practice.

Conclusion

Applying the original ETRO-trained UC-SCALE model to a real-world UC cohort confirmed its high accuracy and reliability for objective MCES scoring outside of clinical trials. To date, our analysis has focused on predictions at the level of anatomical segments. Future work will expand toward full video-level scoring (closer to clinical workflows) and artificial segments, which offer a more granular topological severity map. Additionally, upcoming analyses will explore correlations between UC-SCALE and key IBD biomarkers, as well as direct comparisons with human scoring.

References

[1] Gutierrez-Becker, Benjamin, et al. "Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE): An Artificial Intelligence Scoring System for a Spatial Assessment of Disease Severity in Ulcerative Colitis." Journal of Crohn's and Colitis 19.1 (2025): jjae187.

Disclosure

Alvaro Gomariz, Bernhard Stimpel, Benjamin Gutierrez-Becker, Stefan Frässle, Emily Fisher, Steven Levitte and Teresa Karrer are employees of F. Hoffmann-La Roche Ltd.

Introduction

Placebo and treatment response rates in randomized ulcerative colitis (UC) trials impact clinical trial design and patients required to demonstrate efficacy. Prior efforts to predict endoscopic healing (EH) in UC trial patients using clinical measures have failed to produce meaningful predictive power. Using AI analysis of baseline endoscopy videos from the ustekinumab UNIFI registration Phase 3 trial in moderate to severe UC, we demonstrate robust accuracy to predict 8 week endoscopic healing in ulcerative colitis patients treated with placebo or drug.

Aims & Methods

300 screening endoscopy videos were obtained from UC patients receiving placebo, and 606 screening endoscopy videos were obtained from UC patients receiving ustekinumab (130mg or 6mg/kg) in the UNIFI induction trial. For all videos, visual features were extracted using EndoDINO, an AI foundation model pre-trained on the largest reported endoscopy video dataset. For each patient, 21 baseline clinical measures were also obtained as predictors. These included fecal calprotectin, albumin, C-reactive protein, lactoferrin, Geboes grading, prior biologics failure, prior steroids failure, UCEIS scores, and Mayo scores.
For each of the placebo and drug arms, we trained two binary classification models: one using the AI visual features and the other using the baseline clinical measures as predictors. Each model was trained to predict 8 week EH, defined as an endoscopy subscore of the Mayo score equal to 0 or 1. We performed 5-fold cross validation and reported the average Area Under the Receiver Operating Curve (AUROC).
This study, carried out under YODA Project # 2024-0668, used data obtained from the Yale University Open Data Access Project, which has an agreement with JANSSEN RESEARCH & DEVELOPMENT, L.L.C.. The interpretation and reporting of research using this data are solely the responsibility of the authors and does not necessarily represent the official views of the Yale University Open Data Access Project or JANSSEN RESEARCH & DEVELOPMENT, L.L.C.. The original proposal can be found: https://yoda.yale.edu/data-request/2024-0668/.

Results

For the placebo arm, our best performing model using AI visual features predicted 8 week endoscopic healing with an AUROC of 0.78, compared to 0.70 for our best performing model using the combination of 21 baseline clinical measures.
For the drug arm, our best performing model using AI visual features predicted 8 week endoscopic healing with an AUROC of 0.75, compared to 0.72 for our best performing model using the combination of 21 baseline clinical measures.
For models using baseline clinical measures, table 1 shows the importance factor for select measures across arms, calculated using XGBoost built in feature importance.

Conclusion

Our results demonstrate AI analysis of baseline colonoscopy videos with the EndoDINO AI foundation model provides robust accuracy for predicting 8 week EH in ulcerative colitis patients in both placebo and drug arms. AI placebo response prediction could be used in future trial design as covariate analysis to increase statistical power or reduce necessary sample size.
In future work, we aim to improve predictive power by combining our AI visual features with the most important baseline clinical measures through different modeling techniques.

References

Sands BE, Sandborn WJ, Panaccione R, et al. Ustekinumab as induction and maintenance therapy for ulcerative colitis. N Engl J Med. 2019;381(13):1201–1214. doi:10.1056/NEJMoa1900750

Disclosure

AK, PD, MC, and MS are employees of Virgo Surgical Video Solutions, Inc., which developed technology used in this work.

Introduction

The clinical benefit of Proactive Therapeutic Drug Monitoring (TDM) for infliximab and other monoclonal antibodies in Inflammatory Bowel Disease (IBD) remains a subject of ongoing debate. This study aimed to assess the real-world impact of broadly applying proactive TDM in unselected, consecutive IBD patients receiving infliximab infusions (both induction and maintenance). We compared TDM-guided management decisions to those made by an IBD expert blinded to TDM results but fully informed otherwise.

Aims & Methods

Infliximab trough levels and anti-drug antibodies were assessed using a validated ELISA assay; Through levels above 10 mg/ml were considered therapeutic. Infliximab levels and antibodies were measured in 55 IBD patients (34 with Crohn’s Disease, 21 with Ulcerative Colitis) in 77 occasions: median age 42 years, median disease duration 8.5 years, mostly bio-exposed (58%) and mostly already on dose-escalated regimens (90%). Crohn’s Disease patients had mostly complicated (stricturing or penetrating) disease (74%), half had prior surgery (44%), 50% were smokers and 20% had perianal disease. Management decisions based on TDM were compared to those of a blinded expert. Patients were followed prospectively for clinical, biochemical, and endoscopic response, with endoscopic data available in 39 cases.

Results

Therapeutic infliximab levels were observed in 80% of samples and anti-drug antibodies were detected in only one patient. Following TDM-based adjustment showed most patients had clinical (80%), biologic (89%) and endoscopic (61%) improvement compared to baseline assessment. Concordance in decision making was achieved in 58% of occasions. Therapy was discontinued in 11 patients (blinded expert concurred in 3/11,27%), the same dose was maintained in 53 patients (agreement occurred in 36/53, 68%), and dose modification in 13 patients (agreement was seen in 6/13, 46%).

Conclusion

Excellent results were achieved in this difficult to treat group of IBD patients treated with iv infliximab, possibly aided by proactive TDM. Without proactive TDM a small but significant percentage of them would risk undertreatment, either by not optimizing or by staying on redundant therapy. Discrepancies between TDM-based decisions and expert clinical judgment suggest that TDM provides distinct and actionable information that may prevent both under- and overtreatment. Future studies should estimate the impact of proactive TDM in different clinical scenarios, mostly in patients with severe disease.

Disclosure

The authors have no conflicts of interest to declare