SynthesisAbdominal radiology (New York)2024
Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review.
Synthesis in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Semi-Automatic Assessment of Crohn's Disease Activity by Combined Analysis of Bowel Lesions and Creeping Fat.Magnetic resonance in medicine · 2026Article
- Imaging endpoints in inflammatory bowel disease: current evidence and future directions for ultrasound and MRI.BMC medicine · 2026Review
- Role of MRI radiomics in deep learning-based prediction of intestinal diseases.International journal of colorectal disease · 2026Review
- Sequence-robust MRI asymmetry measures for the detection of anoperineal lesions in Crohn's disease on axial pelvic MRI scans.PloS one · 2026Article
- Applications of artificial intelligence in abdominal imaging.Abdominal radiology (New York) · 2025Review
- Beyond Fixed Thresholds: Cluster-Derived MRI Boundaries Improve Assessment of Crohn's Disease Activity.Journal of clinical medicine · 2025Article
- The Importance of Magnetic Resonance Enterography in Monitoring Inflammatory Bowel Disease: A Review of Clinical Significance and Current Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- A systematic review on deep learning in ventricular septal defect screening, diagnosis, and management.Annals of pediatric cardiologyReview
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crohn's disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning in improving MRE for CD assessment. We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity. The study was conducted according to the PRISMA guidelines. The risk of bias was evaluated using the QUADAS-2 tool. Five eligible studies, encompassing 468 subjects, were identified. Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation for disease burden quantification, and 3D reconstruction for surgical planning are useful and promising for CD assessment. However, most of the studies are preliminary, retrospective studies, and have a high risk of bias in at least one category. Future research is needed to assess how deep learning can impact CD patient diagnostics, particularly when considering the increasing integration of such models into hospital systems.
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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.