Artificial Intelligence-Based Classification and Prognostic Prediction in Small Bowel Crohn's Disease

Small bowel involvement is common in Crohn's disease and is associated with an increased risk of strictures, penetrating complications, and surgery. Because conventional ileocolonoscopy cannot fully assess most small bowel segments or transmural and extraintestinal abnormalities, computed tomography enterography (CTE) and magnetic resonance enterography (MRE) play important roles in evaluating small bowel Crohn's disease. However, interpretation of these images may vary among observers, and conventional imaging assessment may not fully quantify the complex intestinal and mesenteric features associated with treatment response and disease progression.

This study will use an interactive artificial intelligence-based image segmentation method to identify and quantify small bowel lesions on existing CTE or MRE images. The imaging features of interest include the number and length of affected bowel segments, bowel wall thickness and enhancement, luminal narrowing, prestenotic dilatation, inflammatory or fibrotic characteristics of strictures, creeping fat, comb sign, internal fistulas, and intra-abdominal abscesses. Where available, pathological features from endoscopic biopsy or surgical specimens and molecular features, including tissue RNA expression and cytokine measurements, will also be analyzed.

The imaging features will be integrated with clinical information, including demographic characteristics, disease duration, disease location and behavior, perianal disease, previous Crohn's disease-related surgery, clinical and endoscopic disease activity, nutritional status, laboratory findings, and treatment history. Multimodal data analysis will be used to establish a classification system for small bowel Crohn's disease and to develop a model for predicting subsequent clinical outcomes.

Patients will be categorized according to their clinical course as having an effective response to medical treatment, treatment failure or recurrence requiring a treatment switch, or requiring Crohn's disease-related surgery. Univariable and multivariable analyses will be conducted to identify factors associated with these outcomes. The predictive performance of the resulting model will be evaluated using the area under the receiver operating characteristic curve, sensitivity, and specificity. The study is expected to provide an objective tool for disease classification, risk assessment, and individualized clinical decision-making in patients with small bowel Crohn's disease.

Inclusion criteria

• Diagnosis of Crohn's disease established according to the European Crohn's and Colitis Organisation criteria based on clinical, endoscopic, radiological, and/or histopathological findings.
• Small bowel involvement confirmed by computed tomography enterography, magnetic resonance enterography, endoscopy, surgery, and/or histopathology.
• Availability of CTE or MRE images obtained before treatment and during follow-up. Follow-up imaging was performed within 6 months after treatment for patients with active disease or within 1 to 2 years for patients in remission.
• Availability of sufficient clinical and follow-up information to determine treatment response, treatment switching, or Crohn's disease-related surgery.

Exclusion criteria

• Failure to receive regular medical treatment or follow-up.
• Incomplete clinical or laboratory data that prevent assessment of the prespecified variables or clinical outcomes.
• Poor-quality or incomplete CTE or MRE images that prevent reliable image segmentation or evaluation.
• Presence of a malignant tumor.
• Presence of severe comorbidities, including heart failure or other severe organ dysfunction, that may substantially affect clinical outcomes.