ArticleInsights into imaging2025
Predicting mucosal healing in Crohn's disease: development of a deep-learning model based on intestinal ultrasound images.
Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- TSNet: a multi-modal deep learning framework for subtyping appendicitis: integrating ultrasound images, handcrafted features, and clinical data.Abdominal radiology (New York) · 2026Article
- Integrating Intestinal Ultrasound in the Personalized Management of IBD.Journal of personalized medicine · 2026Review
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.International journal of general medicine · 2026Review
- Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances.World journal of gastroenterology · 2025Review
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Authors and funding
13 authors.
Funding
Abstract
objectivePredicting treatment response in Crohn's disease (CD) is essential for making an optimal therapeutic regimen, but relevant models are lacking. This study aimed to develop a deep learning model based on baseline intestinal ultrasound (IUS) images and clinical information to predict mucosal healing.
methodsConsecutive CD patients who underwent pretreatment IUS were retrospectively recruited at a tertiary hospital. A total of 1548 IUS images of longitudinal diseased bowel segments were collected and divided into a training cohort and a test cohort. A convolutional neural network model was developed to predict mucosal healing after one year of standardized treatment. The model's efficacy was validated using the five-fold internal cross-validation and further tested in the test cohort.
resultsA total of 190 patients (68.9% men, mean age 32.3 ± 14.1 years) were enrolled, consisting of 1038 IUS images of mucosal healing and 510 images of no mucosal healing. The mean area under the curve in the test cohort was 0.73 (95% CI: 0.68-0.78), with the mean sensitivity of 68.1% (95% CI: 60.5-77.4%), specificity of 69.5% (95% CI: 60.1-77.2%), positive prediction value of 80.0% (95% CI: 74.5-84.9%), negative prediction value of 54.8% (95% CI: 48.0-63.7%). Heat maps showing the deep-learning decision-making process revealed that information from the bowel wall, serous surface, and surrounding mesentery was mainly considered by the model.
conclusionsWe developed a deep learning model based on IUS images to predict mucosal healing in CD with notable accuracy. Further validation and improvement of this model with more multi-center, real-world data are needed. CRITICAL RELEVANCE STATEMENT: Predicting treatment response in CD is essential to making an optimal therapeutic regimen. In this study, a deep-learning model using pretreatment ultrasound images and clinical information was generated to predict mucosal healing with an AUC of 0.73. KEY POINTS: Response to medication treatment is highly variable among patients with CD. High-resolution IUS images of the intestinal wall may hide significant characteristics for treatment response. A deep-learning model capable of predicting treatment response was generated using pretreatment IUS images.
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