ArticleInflammatory bowel diseases2020
Assessing Small Bowel Stricturing and Morphology in Crohn's Disease Using Semi-automated Image Analysis.
Article in Inflammatory bowel diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 66 citations in OpenAlex.
- Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn's Disease and Ulcerative Colitis by CT Image.Computational and mathematical methods in medicine · 2022Trial
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- Scleral image analysis via artificial intelligence to evaluate inflammatory status in inflammatory bowel disease.International journal of colorectal disease · 2026Article
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Quantitative MRI radiomics approach for evaluating muscular alteration in Crohn disease: development of a machine learning-nomogram composite diagnostic tool.Abdominal radiology (New York) · 2025Article
- Comparison of volumetric and linear measurements of intestinal inflammation and treatment response in children with newly diagnosed ileal Crohn disease.European radiology · 2025Article
- Artificial Intelligence-Enabled Clinical Trials in Inflammatory Bowel Disease: Automating and Enhancing Disease Assessment and Study Management.Gastroenterology · 2025Review
- Artificial intelligence in inflammatory bowel disease.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Review
- How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Review
- Comparison of Radiomics and Deep Learning Using Intestinal Ultrasound Images for Differentiating Inflammatory and Fibrotic Intestinal Strictures in Crohn's Disease.Journal of inflammation research · 2025Article
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.Therapeutic advances in gastroenterology · 2025Review
- The role of molecular imaging in detecting fibrosis in Crohn's disease.Annals of medicine · 2024Review
- Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.BMC medical imaging · 2024Article
- Local Assessment and Small Bowel Crohn's Disease Severity Scoring using AI.Academic radiology · 2024Article
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- Magnetic Resonance Enterography and Intestinal Ultrasound for the Assessment and Monitoring of Crohn's Disease.Journal of Crohn's & colitis · 2024Review
- Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.Clinical imaging · 2024Article
- Navigating the complexities of drug development for inflammatory bowel disease.Nature reviews. Drug discovery · 2024Review
- Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?Current gastroenterology reports · 2024Review
- Current Developments and Role of Intestinal Ultrasound including the Advent of AI.Diagnostics (Basel, Switzerland) · 2024Review
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8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundEvaluating structural damage using imaging is essential for the evaluation of small intestinal Crohn's disease (CD), but it is limited by potential interobserver variation. We compared the agreement of enterography-based bowel damage measurements collected by experienced radiologists and a semi-automated image analysis system.
methodsPatients with small bowel CD undergoing a CT-enterography (CTE) between 2011 and 2017 in a tertiary care setting were retrospectively reviewed. CT-enterography studies were reviewed by 2 experienced radiologists and separately underwent automated computer image analysis using bowel measurement software. Measurements included maximum bowel wall thickness (BWT-max), maximum bowel dilation (DIL-max), minimum lumen diameter (LUM-min), and the presence of a stricture. Measurement correlation coefficients and paired t tests were used to compare individual operator measurements. Multivariate regression was used to model identification of strictures using semi-automated measures.
resultsIn 138 studies, the correlation between radiologists and semi-automated measures were similar for BWT-max (r = 0.724, 0.702), DIL-max (r = 0.812, 0.748), and LUM-min (r = 0.428, 0.381), respectively. Mean absolute measurement difference between semi-automated and radiologist measures were no different from the mean difference between paired radiologists for BWT-max (1.26 mm vs 1.12 mm, P = 0.857), DIL-max (2.78 mm vs 2.67 mm, P = 0.557), and LUM-min (0.54 mm vs 0.41 mm, P = 0.596). Finally, models of radiologist-defined intestinal strictures using automatically acquired measurements had an accuracy of 87.6%.
conclusionStructural bowel damage measurements collected by semi-automated approaches are comparable to those of experienced radiologists. Radiomic measures of CD will become an important new data source powering clinical decision-making, patient-phenotyping, and assisting radiologists in reporting objective measures of disease status.
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