To develop a non-contact precision monitoring system based on machine learning and computer vision technology，enabling continuous and objective assessment of flap perfusion status and early warning of vascular crisis.

From March 2024 to April 2025，84 patients who underwent free flap transplantation in the Department of Hand Surgery at Beijing Jishuitan Hospital，Capital Medical University were enrolled. Videos of the flap transplanted areas were recorded during the first 72 hours postoperatively. A dataset comprising 580 videos was constructed and randomly divided into training set and test set at a ratio of 8∶2. The Mask R-CNN model was employed for automatic flap segmentation and tracking. Eight features，including color，texture，and photoplethysmographic signals，were extracted，and a risk warning model was established based on ensemble learning algorithms. The system's performance was compared with conventional manual monitoring for validation.

The system demonstrated high segmentation accuracy on the test set, the average precision reached 1.000 at an intersection over union of 0.75，and the classification model achieved an accuracy of 0.851±0.085. For the two patients who developed vascular crisis in the test set，the model provided earlier warnings than conventional manual monitoring in all cases.

The artificial intelligence–based monitoring system constructed in this study enables non-invasive，continuous，and precise assessment of flap perfusion，enhances warning timeliness and clinical practicality，and holds potential for reducing the risk of flap necrosis and improving the quality of postoperative management.