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.