Clinical Report: Forecasting Models for Bowel Obstruction: Transitioning from Clinical Assessments to AI Technologies
Overview
This review summarizes the evolution of prediction models for intestinal obstruction, highlighting the transition from traditional clinical assessments to advanced AI technologies.
Background
Intestinal obstruction is a prevalent surgical emergency that can lead to severe complications if not promptly recognized and treated. Accurate prediction models are essential for risk stratification and guiding urgent interventions.
Data Highlights
No numerical data or trial data available in the source material.
Key Findings
Conventional clinical scores are transparent and inexpensive but vary in performance across different settings.
CT-integrated models enhance anatomical and ischemic risk assessment but rely on imaging availability and reader expertise.
Machine-learning and deep-learning models show high discrimination in selected datasets but often lack external validation.
The review proposes a three-generation framework for prediction models: bedside clinical scores, CT-integrated models, and AI-enabled systems.
Immediate research priorities include prospective multicenter validation and standardized endpoint definitions.
Clinical Implications
Healthcare professionals should be aware of the limitations of existing prediction models.
Conclusion
Continued research and validation are crucial for optimizing these tools in emergency surgical care.
