Surgeon Supervised Autonomous Surgical System for Oral and Maxillofacial Surgery

Oral and maxillofacial surgery (OMS) imposes an increasing workload on even the most experienced surgeons due to long operation time, high skill requirements, limited observation field, constrained workspace, and fast-growing patient population. Robot-assisted OMS is particularly challenging, requiring technological advancements to replicate complex surgical workflows executed by human surgeons and novel working concepts to properly address human-machine relationships. We introduced a Surgeon Supervised Autonomous Surgical System (SSASS) aiming to solve emerging bottlenecks in OMS. SSASS custom develops a deep-learning-assisted virtual planning module, a teeth-based monocular camera navigation module, and a six-degree-of-freedom compact robot module to function as surgeons’ auxiliary brain, eye, and hand, respectively. These three modules are further seamlessly integrated to autonomously complete most labor-intensive procedures, while prioritizing surgeons to supervise and be responsible for the overall procedure. Le Fort I experiments on five human head models demonstrated that the surgical results of SSASS closely matched the preoperative plan, with high drilling accuracy and acceptable cutting accuracy under a fundamentally new and significantly simplified surgical workflow. Compared to its existing OMS counterparts, SSASS integrates the latest technologies such as deep learning, medical 3D printing, markerless navigation, virtual reality, and collaborative robotics, providing a comprehensive surgical solution for encompassing the entire OMS loop. Note to Practitioners—This study was motivated by the increasing needs of automating the surgical procedure of oral and maxillofacial surgery (OMS) to reduce the mismatch between the inadequate number of professional surgeons and the fast-growing patient population. Although more and more surgical hardware and software were introduced into the operating room to assist the surgeon, their workloads are only partially alleviated as surgeons still have to perform many vital procedures manually. This study proposed a Surgeon Supervised Autonomous Surgical System (SSASS) with a significantly simplified workflow for achieving predictable, controllable, and repeatable OMS outcomes. SSASS could avoid many previously necessary analyzing procedures and surgical tools, freeing surgeons from the need to conduct time-consuming manual cephalometric analysis, prefabricate physical surgical models, primary robot programming knowledge or repeatedly program robot trajectory in every surgery, and use physical tracking markers and frequently measure the separated skeleton parts. SSASS introduced a systematical surgical solution aiming to finish most high-workload, tedious, repetitive work while giving the highest priority to surgeons for surveillance. We disclosed key software and hardware information of this system in the manuscript and supplementary materials so that the practitioners could rebuild or improve our current prototype without major difficulties. The experiment results show the high application potential of this system in OMS.