ArticleUnited European gastroenterology journal2025
An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.
Article in United European gastroenterology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.International journal of general medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
backgroundCrohn's disease (CD) is an incurable inflammatory bowel disease that can lead to a variety of complications and requires lifelong treatment. However, the diagnosis and management of Crohn's disease exhibit high rates of misdiagnosis and missed diagnoses, along with significant variability, among primary care facilities and novice endoscopists. Therefore, we established an interpretable artificial intelligence (AI) system using double-balloon enteroscopy to facilitate Crohn's disease ulcer identification and grading.
objectiveTo develop an interpretable AI system for the identification and grading of Crohn's disease ulcer images, offering bounding box localization for visual interpretability and factor-specific grading explanations for each ulcer to improve assessment performance.
methodsWe constructed a region and grading model of individual ulcers based on the YOLO-v5 algorithm. By analyzing the predicted results of all ulcers in each image, the clinical interpretation for the screening and assessment of Crohn's disease ulcer images was further achieved. To evaluate the system, we prepared the training and validation datasets (17,036 double-balloon enteroscopy images, 558 patients) and further collected a test cohort (2018 images, 70 patients) and an external validation set. A further reader study was conducted on the internal test set in which nine endoscopists participated to evaluate the auxiliary effectiveness of the explainable system.
resultsThe Crohn's disease ulcer image detection sensitivity and area under the curve (AUC) were 91.8% and 0.949. The accuracies in assessing the severity of Crohn's disease ulcer images on three factors (size/ulcerated surface/depth) were 94.1%/92.5%/93.0%, respectively. With the system's support of visualized and analyzable predictions, junior endoscopists improved their Crohn's disease ulcer image recognition sensitivity by 12.7% and their accuracy and consistency of severity assessment by 26% and 27.4%.
conclusionThe AI system outperformed general endoscopists in approaching expert-level proficiency in Crohn's disease ulcer identification and assessment. Its transparency in decision-making facilitated integration into clinical workflows, enhancing trust and consistency among endoscopists.
Indexed as
Identifiers
What OpenQuestion holds
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.