Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel
2026-06-01 • Artificial Intelligence
Artificial IntelligenceComputer Vision and Pattern RecognitionInformation RetrievalMachine Learning
AI summaryⓘ
The authors aim to create an AI tool to predict risks of a serious complication called anastomotic leak after colorectal cancer surgery by analyzing special CT scans taken before surgery. Their method involves collecting and carefully handling patient data, then using deep learning to both estimate leak risk and find similar past cases to help doctors decide how to proceed. They show that this approach can be integrated into current hospital systems and suggest other groups can copy the method to build similar tools. Overall, the authors want to improve surgery planning and make decisions more data-driven and understandable for doctors.
anastomotic leakcolorectal cancer surgerycomputed tomography (CT)deep learningpreoperative risk assessmentvascular featuresContent-Based Medical Image Retrieval (CBMIR)GDPRclinical decision supportexplainable AI
Authors
Zahra Tabatabaei, Jon Sporring, Mark Bremholm Ellebæk, Alaa El-Hussuna
Abstract
Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery trajectories, and healthcare costs. Despite advances in imaging technology, current preoperative assessment relies only on clinical assessment, a process that is subjective, error-prone, and highly dependent on individual expertise. To date, no validated CT-based method exists to predict anastomotic leak risk prior to surgery. This protocol paper outlines a comprehensive framework for developing and validating an AI-driven system for preoperative risk assessment using pre- and post-contrast CT imaging. The study describes the stages of data collection, ethical handling, and preprocessing of patient data in accordance with GDPR, image preprocessing, and the exploration of deep learning architectures designed to generate clinically interpretable outputs. Two integrated tools constitute the main deliverables of this workflow: 1) a risk assessment module, which quantifies the likelihood of leakage by analyzing vascular and tissue features in CT scans, and 2) a Content-Based Medical Image Retrieval (CBMIR) module, which identifies and displays similar historical cases to support evidence-based surgical decision making. The protocol paper requires close collaboration between hospitals and universities; this protocol demonstrates that such a system is technically feasible and clinically implementable within existing healthcare infrastructures. By following the proposed methodological stages and regulatory principles, other institutions can reproduce this workflow to develop analogous decision-support tools. Ultimately, this interdisciplinary framework aims to enhance surgical planning, reduce leak incidence, and contribute to a broader paradigm shift toward explainable, data-driven precision surgery.