AI models aim to improve risk stratification for strangulated small bowel obstruction

Strangulated small bowel obstruction (SSBO) is a life-threatening surgical emergency. Current clinical assessment, which predominantly relies on static and isolated parameters, often fails to accurately identify the critical transition from reversible ischemia to irreversible bowel necrosis. This diagnostic gap often results in delayed recognition and suboptimal timing of surgical intervention. Consequently, early and accurate risk stratification is imperative to guide clinical decision-making. The field is currently shifting from static evaluations toward dynamic, continuous predictive models. This narrative review examines the paradigm shift in SSBO risk assessment—from static, single-timepoint tools to integrated, intelligent systems capable of analyzing temporal data, while critically examining their current limitations in clinical validation and real-world applicability. We evaluate the characteristics and clinical applicability of various risk-stratification instruments, with a focused discussion on the role of artificial intelligence (AI) and machine learning (ML) in processing multimodal time-series data. Ultimately, we aim to outline a framework for standardized risk stratification to enhance triage precision and to advance SSBO management from a predominantly experience-based practice toward a standardized, evidence-based approach.