Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

Abstract

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.