A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification.

Histological assessment is essential for the diagnosis and management of celiac disease. Current scoring systems, including modified Marsh (Marsh-Oberhuber) score, lack inter-pathologist agreement. To address this unmet need, we aimed to develop a fully automated, quantitative approach for histology characterisation of celiac disease.

Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis.

Backgrounds And Aims: Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials.

Methods: We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?".

Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response.

Background: Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e.

Identifying Measures of Suboptimal Healthcare Interaction (SOHI) to Develop a Claims-Based Model for Predicting Patients with Inflammatory Bowel Disease at Risk for SOHI.

Background: Understanding the demographic and clinical characteristics of patients with Inflammatory Bowel Disease (IBD) who are likely to experience poor disease outcomes may allow early interventions that can improve health outcomes.

Objectives: To describe demographic and clinical characteristics of patients with ulcerative colitis (UC) and Crohn's disease (CD) with the presence of at least one Suboptimal Healthcare Interaction (SOHI) event, which can inform the development of a model to predict SOHI in members with IBD based on insurance claims, with the goal of offering these patients some additional intervention.

Methods: We identified commercially insured individuals with IBD between 01 January 2019 and 31 December 2019 using Optum Labs' administrative claims database.