Predictive models for intestinal obstruction: from clinical scores to artificial intelligence.

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Abstract

Intestinal obstruction is a common surgical emergency in which delayed recognition of strangulation, ischemia, or failure of non-operative management can lead to bowel necrosis, sepsis, and death. Prediction models have been developed for several related but distinct tasks, including diagnosis of obstruction, prediction of urgent surgery, prediction of strangulation or irreversible ischemia, and prediction of failure of conservative treatment. This review critically summarizes these models and clarifies their clinical scope, validation status, and translational limitations. PubMed was searched for studies published from database inception to October 31, 2025, with the initial search performed during manuscript preparation on December 1, 2025 and the final update search conducted on June 18, 2026. Search terms were related to intestinal obstruction, small bowel obstruction, prediction model, nomogram, scoring system, artificial intelligence, machine learning, deep learning, and computed tomography. Eligible articles reported or discussed predictive, diagnostic, or decision-support models for intestinal obstruction. We excluded papers without model-related content, non-clinical mechanistic studies unless used to explain modeling rationale, case reports, and articles not providing sufficient methodological or performance information. Because the evidence was heterogeneous in population, endpoint, modality, and design, the review was synthesized narratively rather than meta-analyzed. Conventional clinical scores remain attractive because they are transparent, inexpensive, and quickly calculable, but their performance varies across endpoints and settings. CT-integrated models improve anatomical and ischemic risk assessment but depend on imaging availability and reader expertise. Machine-learning and deep-learning models, including multimodal systems combining electronic health records and imaging, have reported high discrimination in selected datasets; however, many studies remain retrospective, single-center, and incompletely externally validated. Therefore, AUC values across studies should not be interpreted as directly comparable evidence of superiority. The field is moving from static, single-modality scores toward dynamic, multimodal decision-support systems. The most immediate research priorities are prospective multicenter validation, standardized endpoint definitions, calibration and decision-curve reporting, explainability, fairness assessment, privacy-preserving data sharing, and workflow integration in emergency surgical care.

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