Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel
arXiv:2606.02156v1 Announce Type: cross 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
The paper outlines a protocol for developing AI-driven preoperative assessment, signaling a critical move from subjective clinical judgment to objective, CT-based methods in surgical planning.
Improving the accuracy of predicting surgical complications like anastomotic leaks can significantly reduce patient morbidity, healthcare costs, and enhance surgical outcomes.
Preoperative assessment for colorectal surgery could shift from highly subjective clinical evaluation to more standardized, AI-assisted methodologies based on imaging data.
- · AI medical imaging companies
- · Healthcare providers
- · Colorectal surgery patients
- · Medical AI researchers
- · Traditional diagnostic methods
- · Healthcare systems with slow AI adoption
Immediate reduction in colorectal anastomotic leak rates.
Increased adoption of AI in other surgical risk prediction and personalized medicine fields.
Potential for AI to transform surgical training and accreditation, emphasizing data-driven clinical reasoning.
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Read at arXiv cs.LG