Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods.

BACKGROUND: Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. METHODS: Clinical data and postoperative flap photos of head and neck cancer patients (2018-2024) at our center were retrospectively collected, categorized into normal and venous-insufficient groups. Eight machine learning classifiers and three deep learning models (ResNet, GoogleNet, Densenet) were built. SHAP and Grad-CAM visualization were used for feature analysis and validation. RESULTS: A total of 2575 flap images from 576 patients (2010 normal, 565 venous-insufficient) were analyzed. Random Forest performed best in machine learning (accuracy 90.25%, AUC 0.759), with SHAP identifying Hue_mean and Green_median as key features. ResNet outperformed in deep learning (accuracy 95.23%, sensitivity 84.81%, specificity 97.27%, AUC 0.940). CONCLUSION: The deep learning model shows good value in identifying flap venous insufficiency, serving as an auxiliary tool for postoperative monitoring.