Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery

Abstract

Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and machines. Digitization of operating rooms and the creation of massive quantities of data have paved the way for machine learning and computer vision applications in surgery. Surgical phase recognition (SPR) is a newly emerging technology that uses data derived from operative videos to train machine and deep learning algorithms to identify the phases of surgery. Advancement of this technology will be key in establishing context-aware surgical systems in the future. By automatically recognizing and evaluating the current surgical scenario, these intelligent systems are able to provide intraoperative decision support, improve operating room efficiency, assess surgical skills, and aid in surgical training and education. Still in its infancy, SPR has been mainly studied in laparoscopic surgeries, with a contrasting stark lack of research within neurosurgery. Given the high-tech and rapidly advancing nature of neurosurgery, we believe SPR has a tremendous untapped potential in this field. Herein, we present an overview of the SPR technology, its potential applications in neurosurgery, and the challenges that lie ahead.

Keywords: Artificial intelligence; Deep learning; Machine learning; Neurosurgery; Surgery; Surgical phase recognition.