• Acute Severe Ulcerative Colitis
• Randomized Controlled Clinical Trial
• Trial Design
• Ulcerative Colitis

• U01 DK134321 NIDDK NIH HHS

• Endoscopic scores
• Fecal calprotectin
• Histological healing
• Inflammatory bowel diseases
• Mucosal healing
• Ulcerative colitis

• Humans
• Leukocyte L1 Antigen Complex/analysis
• Feces/chemistry
• Intestinal Mucosa/pathology
• Intestinal Mucosa/metabolism
• Biomarkers/analysis
• Biomarkers/metabolism
• Inflammatory Bowel Diseases/pathology
• Inflammatory Bowel Diseases/metabolism
• Inflammatory Bowel Diseases/therapy
• Severity of Illness Index
• Wound Healing
• Colonoscopy
• Disease Progression
• Recurrence
• Endoscopy, Gastrointestinal/methods

• Clinical trial
• Colorectal cancer
• Colorectal neoplasms
• Endoscopy
• Inflammatory bowel disease

• Humans
• Inflammatory Bowel Diseases/diagnosis
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/epidemiology
• Colonoscopy
• Endoscopy, Gastrointestinal/methods

• Crohn's disease
• Endoscopy
• Histopathology
• Inflammatory bowel diseases
• Ulcerative colitis

• clinical trials
• endoscopy
• inflammatory bowel disease

• Ultrasound
• activity index
• monitoring
• reliability

• Consensus
• Crohn Disease/diagnostic imaging
• Delphi Technique
• Humans
• Reproducibility of Results
• Ultrasonography/standards

• Computer Vision
• Efficacy End Points
• Endoscopic Scores
• Machine Learning

Gastroenterologists should accurately describe endoscopic findings and integrate them into management plans. We aimed to determine if trainees and staff are describing inflammatory bowel disease (IBD) lesions in a similar manner.

Using 20 ileocolonoscopy images, participants described IBD inflammatory burden based on physician severity rating, and Mayo endoscopic score (MES) (ulcerative colitis [UC]) or simple endoscopic score (SES-CD) (Crohn’s disease [CD]). Images were selected based on agreement by three IBD experts. Findings of varying severity were presented; 10 images included a question about management. We examined inter-observer agreement among trainees and staff, compared trainees to staff, and determined accuracy of response comparing both groups to IBD experts.

One hundred and twenty-nine staff and 47 trainees participated from across Canada. There was moderate inter-rater agreement using physician severity rating (κ = 0.53 UC and 0.52 CD for staff, κ = 0.51 UC and 0.43 CD for trainees). There was moderate inter-rater agreement for MES for staff and trainees (κ = 0.49 and 0.48, respectively), but fair agreement for SES-CD (κ = 0.37 and 0.32, respectively). For accuracy of response, the mean score was 68.7% for staff and 63.7% for trainees (P = 0.028). Both groups identified healed bowel or severe disease better than mild/moderate (P < 0.05). There was high accuracy for management, but staff scored higher than trainees for UC (P < 0.01).

Inter-rater agreement on description of IBD lesions was moderate at best. Staff and trainees more accurately describe healed and severe disease, and better describe lesions in UC than CD.

• Crohn’s disease
• Endoscopy
• Ulcerative colitis

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications.

In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy.

Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully.

• Artificial intelligence
• Colonoscopy
• Computer assisted diagnosis
• Early detection of cancer
• Endoscopy

• Drug Development
• EMA
• FDA
• Guidelines
• Inflammatory Bowel Disease
• Ulcerative Colitis

• Esophageal varices
• Hepatology
• Portal hypertension
• Survey

• Comparative effectiveness
• Crohn’s disease
• Pragmatic trials
• Strategy

To establish consensual definitions of anoperineal lesions of Crohn’s (APLOC) disease and assess interobserver agreement on their diagnosis between experts.

A database of digitally recorded pictures of APLOC was examined by a coordinating group who selected two series of 20 pictures illustrating the various aspects of APLOC. A reading group comprised: eight experts from the Société Nationale Française de Colo Proctologie group of study and research in proctology and one academic dermatologist. All members of the coordinating and reading groups participated in dedicated meetings. The coordinating group initially conducted a literature review to analyse verbatim descriptions used to evaluate APLOC. The study included two phases: establishment of consensual definitions using a formal consensus method and later assessment of interobserver agreement on the diagnosis of APLOC using photos of APLOC, a standardised questionnaire and Fleiss’s kappa test or descriptive statistics.

Terms used in literature to evaluate visible APLOC did not include precise definitions or reference to definitions. Most of the expert reports on the first set of photos agreed with the main diagnosis but their verbatim reporting contained substantial variation. The definitions of ulceration (entity, depth, extension), anal skin tags (entity, inflammatory activity, ulcerated aspect), fistula (complexity, quality of drainage, inflammatory activity of external openings), perianal skin lesions (abscess, papules, edema, erythema) and anoperineal scars were validated. For fistulae, they decided to follow the American Gastroenterology Association’s guidelines definitions. The diagnosis of ulceration (κ = 0.70), fistulae (κ = 0.75), inflammatory activity of external fistula openings (86.6% agreement), abscesses (84.6% agreement) and erythema (100% agreement) achieved a substantial degree of interobserver reproducibility.

This study constructed consensual definitions of APLOC and their characteristics and showed that experts have a fair level of interobserver agreement when using most of the definitions.

• Crohn’s disease
• Anoperineal lesions
• Fistula
• Interobserver agreement

• Abscess/diagnosis
• Abscess/etiology
• Adult
• Anal Canal/diagnostic imaging
• Anal Canal/pathology
• Clinical Decision-Making
• Colorectal Surgery/psychology
• Consensus
• Crohn Disease/complications
• Crohn Disease/diagnostic imaging
• Endoscopy, Gastrointestinal
• Fissure in Ano/diagnosis
• Fissure in Ano/etiology
• Humans
• Physical Examination
• Practice Guidelines as Topic
• Rectal Fistula/diagnosis
• Rectal Fistula/etiology
• Reproducibility of Results
• Surveys and Questionnaires

Incorrect classification of outcomes in clinical trials can lead to biased estimates of treatment effect and reduced power. Ensuring appropriate adjudication methods to minimize outcome misclassification is therefore essential. While there are many reported adjudication approaches, there is little consensus over which approach is best.

Under the assumption of non-differential assessment (i.e. that misclassification rates are the same in each treatment arm, as would typically be the case when outcome assessors are blinded), we use simulation and theoretical results to address four different questions about outcome adjudication: (a) How many assessors should be used? (b) When is it better to use onsite or central assessment? (c) Should central assessors adjudicate all outcomes, or only suspected events? (d) Should central assessment with multiple assessors be done independently or through group consensus?

No one adjudication approach performs optimally in all settings. The optimal approach depends on the misclassification rates of site and central assessors, and the correlation between assessors. We found: (a) there will generally be little incremental benefit to using more than three assessors and, for outcomes with very high correlation between assessors, using one assessor is sufficient; (b) when choosing between site and central assessors, the assessor with the smallest misclassification rate should be chosen; when these rates are unknown, a combination of one site assessor and two central assessors will provide good results across a range of scenarios; (c) having central assessors adjudicate only suspected events will typically increase bias, and should be avoided, unless the threshold for sending outcomes for central assessment is extremely low; (d) central assessors can adjudicate either independently or in a group, and the preferred option should be dictated by whichever is expected to have the lowest misclassification rate.

Outcome adjudication is of critical importance to ensure validity of trial results, although no one approach is optimal across all settings. Investigators should choose the best strategy based on the specific characteristics of their trial. Regardless of the adjudication strategy chosen, assessors should be qualified and receive appropriate training.

• Central assessor
• Endpoint adjudication committee
• Endpoint review committee
• Misclassification
• Outcome adjudication
• Outcome assessment
• Randomized controlled trial