Biophysical modeling and artificial intelligence for quantitative assessment of anastomotic blood supply in laparoscopic low anterior rectal resection.

purposeFluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains limited by subjective interpretation and lack of quantitative standards. This study introduces an integrated approach combining fluorescence curve analysis, biophysical modeling, and machine learning to improve intraoperative perfusion assessment.

methodsLaparoscopic fluorescence videos from 68 low rectal cancer patients were analyzed, with 1,263 measurement points (15-20 per case) selected along colonic bands. Fluorescence intensity dynamics were extracted via color space transformation, video stabilization and image registration, then modeled using the Random Sample Consensus (RANSAC) algorithm and nonlinear least squares fitting to derive biophysical parameters. Three clinicians independently scored perfusion quality (0-100 scale) using morphological features and biophysical metrics. An XGBoost model was trained on these parameters to automate scoring.

resultsThe model achieved superior test performance, with a root mean square error (RMSE) of 2.47, a mean absolute error (MAE) of 1.99, and an R

conclusionThis framework synergistically enhances surgical evaluation through three innovations: (1) Biophysical modeling quantifies perfusion dynamics beyond time-based parameters; (2) Machine learning integrates multimodal data for surgeon-level accuracy; (3) Automated workflow enables practical clinical translation. By addressing limitations of visual assessment through quantitative, rapid, and generalizable analysis, this method advances intraoperative perfusion monitoring and decision-making in colorectal surgery.