Using Computer Vision to Improve Endoscopic Disease Quantification in Therapeutic Clinical Trials of Ulcerative Colitis.

BACKGROUND & AIMS: Endoscopic assessment of ulcerative colitis (UC) typically reports only the maximum severity observed. Computer vision methods may better quantify mucosal injury detail, which varies among patients. METHODS: Endoscopic video from the UNIFI clinical trial (A Study to Evaluate the Safety and Efficacy of Ustekinumab Induction and Maintenance Therapy in Participants With Moderately to Severely Active Ulcerative Colitis) comparing ustekinumab and placebo for UC were processed in a computer vision analysis that spatially mapped Mayo Endoscopic Score (MES) to generate the Cumulative Disease Score (CDS). CDS was compared with the MES for differentiating ustekinumab vs placebo treatment response and agreement with symptomatic remission at week 44. Statistical power, effect, and estimated sample sizes for detecting endoscopic differences between treatments were calculated using both CDS and MES measures. Endoscopic video from a separate phase 2 clinical trial replication cohort was performed for validation of CDS performance. RESULTS: Among 748 induction and 348 maintenance patients, CDS was lower in ustekinumab vs placebo users at week 8 (141.9 vs 184.3; P < .0001) and week 44 (78.2 vs 151.5; P < .0001). CDS was correlated with the MES (P < .0001) and all clinical components of the partial Mayo score (P < .0001). Stratification by pretreatment CDS revealed ustekinumab was more effective than placebo (P < .0001) with increasing effect in severe vs mild disease (-85.0 vs -55.4; P < .0001). Compared with the MES, CDS was more sensitive to change, requiring 50% fewer participants to demonstrate endoscopic differences between ustekinumab and placebo (Hedges' g = 0.743 vs 0.460). CDS performance in the JAK-UC replication cohort was similar to UNIFI. CONCLUSIONS: As an automated and quantitative measure of global endoscopic disease severity, the CDS offers artificial intelligence enhancement of traditional MES capability to better evaluate UC in clinical trials and potentially practice.

How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.

Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. METHODS: Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. RESULTS: In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. DISCUSSION: Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Legalization of Cannabis Does Not Reduce Opioid Prescribing in Patients With Inflammatory Bowel Disease: A Difference-in-Difference Analysis.

INTRODUCTION: Cannabis may provide inflammatory bowel disease (IBD) patients with an alternative to opioids for pain. METHODS: We conducted a difference-in-difference analysis using MarketScan. Changes over time in rates of opioid prescribing were compared in states with legalized cannabis to those without. RESULTS: We identified 6,240 patients with IBD in states with legalized cannabis and 79,272 patients with IBD in states without legalized cannabis. The rate of opioid prescribing decreased over time in both groups and were not significantly different (attributed differential = 0.34, confidence interval -13.02 to 13.70, P = 0.96). DISCUSSION: Opioid prescribing decreased from 2009 to 2016 among patients with IBD in both states with legalized and state without legalized cannabis, similar to what has been observed nationally across a variety of diseases. Cannabis legalization was not associated with a lower rate of opioid prescribing for patients with IBD.

Effectiveness of Upadacitinib for Patients With Acute Severe Ulcerative Colitis: A Multicenter Experience.

INTRODUCTION: A significant proportion of patients with acute severe ulcerative colitis (ASUC) require colectomy. METHODS: Patients with ASUC treated with upadacitinib and intravenous corticosteroids at 5 hospitals are presented. The primary outcome was 90-day colectomy rate. Secondary outcomes included frequency of steroid-free clinical remission, adverse events, and all-cause readmissions. RESULTS: Of the 25 patients with ASUC treated with upadacitinib, 6 (24%) patients underwent colectomy, 15 (83%) of the 18 patients with available data and who did not undergo colectomy experienced steroid-free clinical remission (1 patient did not have complete data), 1 (4%) patient experienced a venous thromboembolic event, while 5 (20%) patients were readmitted. DISCUSSION: Upadacitinib along with intravenous corticosteroids may be an effective treatment for ASUC.

Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?

PURPOSE OF REVIEW: Artificial intelligence (AI) is quickly demonstrating the ability to address problems and challenges in the care of IBD. This review with commentary will highlight today's advancements in AI applications for IBD in image analysis, understanding text, and replicating clinical knowledge and experience. RECENT FINDINGS: Advancements in machine learning methods, availability of high-performance computing, and increasing digitization of medical data are providing opportunities for AI to assist in IBD care. Multiple groups have demonstrated the ability of AI to replicate expert endoscopic scoring in IBD, with expansion into automated capsule endoscopy, enterography, and histologic interpretations. Further, AI image analysis is being used to develop new endoscopic scoring with more granularity and detail than is possible using conventional methods. Advancements in natural language processing are proving to reduce laborious tasks required in the care of IBD, including documentation, information searches, and chart review. Finally, large language models and chatbots that can understand language and generate human-like replies are beginning to exhibit clinical intelligence that will revolutionize how we deliver IBD care. Today, AI is being deployed to replicate expert judgement in specific tasks where disagreement, subjectivity, and bias are common. However, the near future will herald contributions of AI doing what we cannot, including new detailed measures of IBD, enhanced analysis of images, and perhaps even fully automating care. As we speculate on future technologic capabilities that may improve how we care for IBD, this review will also consider how we will implement and fairly use AI in practice.

Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.

Development of the Endoscopic Pouch Score for Assessment of Inflammatory Conditions of the Pouch.

Pouchoscopy provides a critical objective measure in the evaluation of patients with suspected inflammatory conditions of the pouch; however, there remain significant gaps in the reliability of the endoscopic scales used in the assessment of these conditions.1,2 Reliability and reproducibility in the assessment of patients after ileal pouch-anal anastomosis (IPAA) are critical, as evidenced by recent efforts to improve standardization in the evaluation of patients with pouch-related disorders.3.

Characteristics of Patients with Crohn's Disease With or Without Perianal Fistulae in the CorEvitas Inflammatory Bowel Disease Registry.

BACKGROUND: There are limited real-world data characterizing perianal fistulae in patients with Crohn's disease (CD). AIM: To describe characteristics of patients with CD with and without perianal fistulae. METHODS: In this cross-sectional study, characteristics, treatment history, and health outcomes of patients with CD enrolled in the CorEvitas IBD Registry were described according to perianal fistula status (current/previous or none). RESULTS: Eight hundred and seventy-eight patients were included. Compared with patients with no perianal fistulae (n = 723), patients with current/previous perianal fistulae (n = 155) had longer disease duration since CD diagnosis (mean 16.5 vs 12.3 years; difference 4.3 years; 95% CI, 2.0, 6.6) and fewer had Harvey-Bradshaw Index scores indicative of remission (0-4, 56.8% vs 69.6%; difference - 12.9%; 95% CI, - 21.6, - 4.2). More patients with current/previous fistulae reported a history of IBD-related emergency room visits (67.7% vs 56.1%; difference 11.6%; 95% CI, 3.4, 19.8), hospitalizations (76.1% vs 58.4%; difference 17.7%; 95% CI, 10.1, 25.4), and surgeries (59.4% vs 27.7%; difference 31.7%; 95% CI, 23.3, 40.1), and a history of treatment with tumor necrosis factor inhibitors (81.3% vs 60.7%; difference 20.6%; 95% CI, 13.5, 27.7), immunosuppressants (51.6% vs 31.2%; difference 20.4%; 95% CI, 11.9, 29.0), and antibiotics (50.3% vs 23.7%; difference 26.6%; 95% CI, 18.2, 35.1) than patients without perianal fistulae. CONCLUSIONS: Patients with CD with current/previous perianal fistulae have more symptomatic experiences of disease, higher medication use, hospitalization rates, and emergency room visits than patients without perianal fistulae. Interventions to prevent/reduce risk of developing fistulae may help improve outcomes in CD.

Efficacy and Safety of Dual Targeted Therapy for Partially or Non-responsive Inflammatory Bowel Disease: A Systematic Review of the Literature.

BACKGROUND: Dual targeted therapy (DTT) has emerged as an attractive therapeutic option for select patients with active inflammatory bowel disease (IBD) who are unable to achieve remission with biologic or small molecule monotherapy. We conducted a systematic review of specific DTT combinations in patients with IBD. METHODS: We conducted a systematic search of MEDLINE, EMBASE, Scopus, CINAHL Complete, Web of Science Core Collection, and Cochrane Library to identify articles related to the use of DTT for the treatment of Crohn Disease (CD) or ulcerative colitis (UC) published before February 2021. RESULTS: Twenty-nine studies were identified comprising 288 patients started on DTT for partially or non-responsive IBD. We identified 14 studies with 113 patients receiving anti-tumor necrosis factor (TNF) and anti-integrin therapies (i.e., vedolizumab and natalizumab), 12 studies with 55 patients receiving vedolizumab and ustekinumab, nine studies with 68 patients receiving vedolizumab and tofacitinib, five studies with 24 patients receiving anti-TNF therapy and tofacitinib, six studies with 18 patients receiving anti-TNF therapy and ustekinumab, and three studies with 13 patients receiving ustekinumab and tofacitinib. CONCLUSION: DTT is a promising approach to improve IBD treatment for patients with incomplete responses to targeted monotherapy. Larger prospective clinical studies are needed to confirm these findings as is additional predictive modeling to identify the patient subgroups most likely to require and benefit from this approach.