Title: Forecasting Colorectal Anastomotic Leak Risk Through Preoperative Bowel Blood Supply Mapping

Anastomotic leakage stands as one of the most severe complications associated with colorectal cancer surgery, significantly impacting patient prognosis, recovery paths, and overall healthcare expenditures. Although imaging technologies have progressed, current preoperative evaluations depend exclusively on clinical judgment. This traditional approach is inherently subjective, susceptible to errors, and heavily reliant on the specific expertise of the practitioner. To date, there is no validated CT-based methodology capable of predicting the risk of anastomotic leaks before surgical intervention.

This protocol paper presents a comprehensive framework for creating and validating an artificial intelligence-driven system designed for preoperative risk assessment. The system utilizes both pre-contrast and post-contrast CT imaging. The document details the various stages involved, including data collection, ethical management, and GDPR-compliant preprocessing of patient information. It also covers image preprocessing techniques and the investigation of deep learning architectures aimed at producing outputs that are clinically interpretable.

The workflow yields two primary integrated tools. The first is a risk assessment module that calculates the probability of leakage by examining vascular and tissue characteristics within CT scans. The second is a Content-Based Medical Image Retrieval (CBMIR) module, which locates and presents analogous historical cases to facilitate evidence-based surgical decisions.

Successful implementation of this protocol necessitates strong collaboration between academic institutions and hospitals. This study demonstrates that such a system is not only technically viable but also practical for integration into existing healthcare environments. By adhering to the outlined methodological steps and regulatory standards, other organizations can replicate this workflow to create similar decision-support instruments. Ultimately, this interdisciplinary approach seeks to improve surgical planning, lower the incidence of leaks, and help drive a broader transition toward precision surgery that is both data-driven and explainable.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC