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 obser- vation field, constrained workspace, and fast-growing patient population. Robot-assisted OMS is particularly challenging, requiring technological advancements to replicate complex sur- gical 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 mod- ule, 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 com- plete 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 preop- erative plan, with high drilling accuracy and acceptable cutting accuracy under a fundamentally new and significantly simplified surgical workflow. Compared to its existing OMS counterparts, Received 2 September 2024; revised 19 March 2025 and 24 June 2025; accepted 7 August 2025. Date of publication 2 September 2025; date of current version 5 September 2025. This article was recommended for publication by Associate Editor I. Godage and Editor X. Liu upon evaluation of the reviewers’ comments. This work was supported in part by Japan Agency for Medical Research and Development under Grant JP191m0203048h002; in part by Japan Society for the Promotion of Science under Grant JP16K11674, Grant JP19K10258, Grant JP26108008, and Grant PDP20106; in part by the National Natural Science Foundation of China under Grant 52205300, Grant U20A20390, and Grant 12332019; and in part by Beijing Natural Science Foundation under Grant NumberL252078. (Qingchuan Ma and Etsuko Kobayashi are co-first authors.) (Corresponding authors: Ken Masamune; Hideyuki Suenaga; Yubo Fan.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Medical Ethics Committee of the University of Tokyo under Application No. 2553-(3). Qingchuan Ma and Yubo Fan are with the School of Engineering Medicine, Beihang University, Beijing 100191, China (e-mail: maqingchuan@bmpe.t. u-tokyo.ac.jp; yubofan@buaa.edu.cn). Etsuko Kobayashi and Kazuaki Hara are with the Graduate School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan (e-mail: etsuko@bmpe.t.u-tokyo.ac.jp; hara@bmpe.t.u-tokyo.ac.jp). Junchen Wang is with the School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China (e-mail: wangjunchen@buaa.edu.cn). Ken Masamune is with the Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University, Tokyo 162-8666, Japan (e-mail: masamune.ken@twmu.ac.jp). Hideyuki Suenaga is with the Department of Oral-Maxillofacial Surgery and Orthodontics, The University of Tokyo Hospital, Tokyo 113-8655, Japan (e-mail: suenaga-tky@umin.net). This article has supplementary downloadable material available at https://doi.org/10.1109/TASE.2025.3604891, provided by the authors. Digital Object Identifier 10.1109/TASE.2025.3604891 SSASS integrates the latest technologies such as deep learning, medical 3D printing, markerless navigation, virtual reality, and collaborative robotics, providing a comprehensive surgical solu- tion for encompassing the entire OMS loop. Note to Practitioners—This study was motivated by the increas- ing 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 pro- cedures manually. This study proposed a Surgeon Supervised Autonomous Surgical System (SSASS) with a significantly sim- plified workflow for achieving predictable, controllable, and repeatable OMS outcomes. SSASS could avoid many previ- ously 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.