Deep Learning Applications in Surgical Video Processing

The surgical field has advanced much during the last two decades, based on continuous technological development that provided impressive improvements in terms of precision, efficiency, and patient care. Deep learning, a vital branch of artificial intelligence in surgical video processing, has been one of the significant and influential developments in this area. This paper reviews deep learning in surgical video processing applications and discusses performance, developments, challenges, and future research directions. It speaks of broad applications, including instrument detection, phase recognition, and video summarization, applied using various deep learning techniques. The review also underlines large, annotated datasets as a requirement to train robust models. While deep learning has taken huge strides within medicine, the paper acknowledges that computational complexity, model interpretability, and data privacy are some of the big challenges. Future perspectives are oriented toward explainable AI, synthetic data generation, and collaborative frameworks to extend deep learning clinically for better surgical efficiency and safety.