Deep Learning-Based Videomics for Automatic Segmentation in Endoscopic Endonasal Surgery

DL-based videomics demonstrates high segmentation accuracy across various pathologies and anatomical targets in endoscopic endonasal surgery. Models such as Swin Transformer and YOLO show potential for real-time surgical support. However, translation into clinical practice remains limited by dataset heterogeneity and variability in reporting.