Surgical Video

Surgical video analysis leverages computer vision and machine learning to automate tasks like instrument tracking, scene segmentation, and phase recognition in surgical procedures. Current research heavily employs deep learning models, including transformers, diffusion models, and various attention mechanisms, often incorporating techniques like self-supervised learning and transfer learning to address data scarcity and improve generalization across different surgical procedures and centers. These advancements aim to improve surgical training, enhance intraoperative decision-making through real-time feedback and guidance (e.g., augmented reality overlays), and ultimately contribute to safer and more efficient surgeries. The development of large, multi-centric datasets is crucial for advancing the field and ensuring robust model performance in real-world clinical settings.