ArticleJournal of magnetic resonance imaging : JMRI2019
Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review.Abdominal radiology (New York) · 2024Pooled it
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Semi-Automatic Assessment of Crohn's Disease Activity by Combined Analysis of Bowel Lesions and Creeping Fat.Magnetic resonance in medicine · 2026Article
- Deep learning-based artificial intelligence can improve the diagnosis of small bowel obstruction: stratified comparison study and hierarchical Bayesian model.Scientific reports · 2026Article
- Comprehensive Imaging Evaluation and Staging of Crohn's Disease: When and Why to Use Intestinal Ultrasound, MRE, or CTE: Current Guidelines and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Role of MRI radiomics in deep learning-based prediction of intestinal diseases.International journal of colorectal disease · 2026Review
- 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
- Discordance between MR enterography and endoscopic detection of Crohn's disease ileal strictures: evidence to inform recommendations.Abdominal radiology (New York) · 2025Article
- Quantitative Magnetic Resonance Imaging (qMRI) of the Small Bowel in Crohn's Disease: State-of-the-Art and Future Directions.Journal of magnetic resonance imaging : JMRI · 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
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Local Assessment and Small Bowel Crohn's Disease Severity Scoring using AI.Academic radiology · 2024Article
- Towards an EKG for SBO: A Neural Network for Detection and Characterization of Bowel Obstruction on CT.Journal of imaging informatics in medicine · 2024Article
- Visual Image Annotation for Bowel Obstruction: Repeatability and Agreement with Manual Annotation and Neural Networks.Journal of digital imaging · 2023Article
- The Role of Radiomics in Fibrosis Crohn's Disease: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.European radiology · 2022Article
- Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?Gastroenterology · 2022Review
- Cross-Sectional Imaging Instead of Colonoscopy in Inflammatory Bowel Diseases: Lights and Shadows.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundContrast-enhanced MRI of the small bowel is an effective imaging sequence for the detection and characterization of disease burden in pediatric Crohn's disease (CD). However, visualization and quantification of disease burden requires scrolling back and forth through 3D images to follow the anatomy of the bowel, and it can be difficult to fully appreciate the extent of disease. PURPOSE: To develop and evaluate a method that offers better visualization and quantitative assessment of CD from MRI. STUDY TYPE: Retrospective. POPULATION: Twenty-three pediatric patients with CD. FIELD STRENGTH/SEQUENCE: 1.5T MRI system and T ASSESSMENT: The convolutional neural network (CNN) segmentation of the bowel's lumen, wall, and background was compared with manual boundary delineation. We assessed the reproducibility and the capability of the extracted markers to differentiate between different levels of disease defined after a consensus review by two experienced radiologists. STATISTICAL TESTS: The segmentation algorithm was assessed using the Dice similarity coefficient (DSC) and boundary distances between the CNN and manual boundary delineations. The capability of the extracted markers to differentiate between different disease levels was determined using a t-test. The reproducibility of the extracted markers was assessed using the mean relative difference (MRD), Pearson correlation, and Bland-Altman analysis.
resultsOur CNN exhibited DSCs of 75 ± 18%, 81 ± 8%, and 97 ± 2% for the lumen, wall, and background, respectively. The extracted markers of wall thickness at the location of min radius (P = 0.0013) and the median value of relative contrast enhancement (P = 0.0033) could differentiate active and nonactive disease segments. Other extracted markers could differentiate between segments with strictures and segments without strictures (P < 0.05). The observers' agreement in measuring stricture length was >3 times superior when computed on curved planar reformatting images compared with the conventional scheme. DATA
conclusionThe results of this study show that the newly developed method is efficient for visualization and assessment of CD. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1565-1576.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.