Cheng-Wei Chen

Robotic technologies are increasingly used in dentistry for their precision in delicate procedures. While most dental robots focus on implant surgery, automating root canal treatment (RCT) remains challenging due to the need to guide a thin, flexible endodontic file through a narrow, curved root canal without causing ledging or file fracture. Patient movements—particularly those that induce additional file bending during insertion—further complicate robot-assisted procedures. This study presents an autonomous approach for root canal cleaning and shaping by combining force admittance and position tracking. A novel Patient Tracking Module, which connects the patient’s dental brace to the robot end-effector via string potentiometers, is developed to estimate real-time robot-patient pose. Additionally, a file flexibility model is proposed to predict and compensate for file deflection during insertion. A hybrid position/force control strategy, which integrates these estimations, autonomously guides file manipulation, minimizes misalignment, and therefore reduces the risk of file fracture. Experimental validation demonstrates the system’s feasibility and potential for clinical application in precision endodontic procedures. Note to Practitioners—This paper addresses the challenge of automating root canal treatment in robot-assisted endodontics, which is complicated by unpredictable patient movements and bending of endodontic files. The proposed system follows a structured workflow: (1) the dentist first diagnoses the case, labels the working length of the canal on preoperative cone-beam computed tomography (CBCT) scans, and performs access opening to the pulp chamber; (2) a custom-designed tooth brace is then installed; (3) the endodontic file held by the robot is aligned with the root canal entrance; (4) the robot is connected to the patient via a string-based Patient Tracking Module (PTM); and (5) the robot autonomously performs canal cleaning and shaping until the working length is reached. The PTM estimates patient movement in real time and compensates for it. Combined with force sensing and file deflection compensation, the system dynamically adjusts the surgical path to maintain alignment and reduce the risk of file fracture. This improves procedural safety, precision, and efficiency. Preclinical tests on acrylic models and 3D-printed teeth show promising results, but further validation in live patient settings is needed. Future work will explore automating additional steps, such as access opening and obturation, to enhance the overall treatment workflow.

This article presents a real-time RGB-D imaging system for robot-assisted remote microsurgery, integrating advanced optics, mechatronics, and dynamic control theory. The system combines a swept-source optical coherence tomography (SS-OCT) probe with a micro RGB camera, exploiting the SS-OCT’s high depth resolution and fast scan rate to generate accurate depth maps co-registered with the camera’s color data. A dual-axis galvanometer scanner, controlled by a fractional-order repetitive control algorithm with a data-driven inverse filter, enables real-time imaging at 30 fps with dynamically adjustable scan areas. Experimental evaluations highlight the system’s exceptional depth-sensing capabilities, including sub-millimeter resolution, precise depth mapping, and smooth dynamic scanning. In addition, the system enhances visual guidance during robotic microsurgery, as validated in teleoperated vessel ligation on a phantom and a cadaveric porcine model.

This study introduces the Laparoscopic Assistive Robotic Manipulator (LapARM), designed to address limitations in current robotic laparoscope holders. LapARM features a compact 4-degree-of-freedom (4-DoF) design achieved through parallel and telescopic mechanisms, synthesized to navigate spatial interference challenges with the human surgeon during surgical operations. Additionally, it improves image stability when zooming with an oblique-viewing scope by developing a custom laparoscope integrated with an embedded distance sensor, allowing the LapARM to dynamically adjust the scope’s orientation during zooming, ensuring that the imaged object remains centered in the image throughout the zooming process. Furthermore, an eye tracker provides surgeons with a contactless interface for intuitive solo control of the laparoscope via head movements. Experimental results validate LapARM’s effective scope maneuvering for laparoscopic procedures. User studies show that its head movement-based control significantly reduces completion time and user workload, contributing to the success of minimally invasive surgeries.

Vision-based haptic feedback provides cost-effective preemptive protection and real-time guidance, enhancing teleoperation with reduced system complexity. However, challenges arise as the instrument approaches target object, leading to occlusion of the point cloud behind the remote instrument, known as the self-occlusion issue. Prior solutions relying on historical point clouds or multiple viewpoints to refill the occluded region encounter adaptability issues for prolonged occlusion and limited space, thus hindering practical implementation. This paper introduces a novel non-refilling-based method for haptic force rendering, leveraging the correspondence between the tool-tip position and the tip position of the shadow-like occluded region. Experimental results demonstrate the proposed method's resilience across self-occlusion and dynamic environments, highlighting its practical applicability in robotic teleoperation.

Robotic technologies are becoming increasingly popular in dentistry due to the high level of precision required in delicate dental procedures. Most dental robots available today are designed for implant surgery, helping dentists to accurately place implants in the desired position and depth. In this paper, we introduce the DentiBot, the first robot specifically designed for dental endodontic treatment. The DentiBot is equipped with a force and torque sensor, as well as a string-based Patient Tracking Module, allowing for real-time monitoring of endodontic file contact and patient movement. We propose a 6-DoF hybrid position/force controller that enables autonomous adjustment of the surgical path and compensation for patient movement, while also providing protection against endodontic file fracture. In addition, a file flexibility model is incorporated to compensate for file bending. Pre-clinical evaluations performed on acrylic root canal models and resin teeth confirm the feasibility of the DentiBot in assisting endodontic treatment.

Background: Subretinal injection (SRI) has become an important surgical method for treating vitreoretinal diseases. Nevertheless, the optimisation of bleb formation in SRI, for the attainment of desired therapeutic outcomes, still requires further investigation.Methods: This study analysed the influence of surgical parameters on SRI using a robotic setup. The surgical procedure was automated using visual guidance. A predictive model for bleb formation was established through regression analysis. To validate the model, we compared the clinical data's target area with the simulated SRI's actual area using parameters determined by the predictive model.Results: The insertion angle dominated the eccentricity and area of the bleb. The injection speed dominated the axial angle. Automated SRI increased success rate and produced predictable outcomes.Conclusions: We could provide accurate SRI on phantom models by adjusting surgical parameters based on the patient's clinical information. Automatic robot‐assisted SRI is a promising surgical technique with highly predictable results.