Due to the large number and complex structure of bronchial branches, frequent orientation and insertions during bronchoscopy are likely to cause surgeon fatigue and errors. Therefore, we designed a robotic-assisted automatic intervention system (RAIS) based on image guidance, which aims to realize automatic orientation and insertion of bronchoscope, for improving the intelligence and efficiency of the bronchoscopy. To realize image guidance, we proposed a highly robust and accurate lumen center detection method, which combines deep learning-based object detection and histogram back-projection. With image guidance of lumen center, RAIS automatically completes the orientation and insertion of bronchoscope. The results of human phantom lung experiments show that the accuracy and recall of proposed lumen center method can respectively reach 94.12% and 86.23% when the bronchoscope is being localized by RAIS. Moreover, the results show that it is feasible to automate the orientation and insertion of bronchoscope using RAIS, which is more efficient than the manual operating the robot. The proposed RAIS could replace the surgeon in performing a large number of repetitive and simple tasks, which can efficiently reduce the workload of the surgeon and improve the safety and efficiency of the procedure.

Robot-assisted minimally invasive surgery (MIS) has shown tremendous advances over the traditional techniques. To improve dexterity and back-drivability of the existing planar remote center-of-motion (RCM) mechanism, on which an active prismatic joint is required to drive the surgical tool move in–out of the patient's body, a two degrees-of-freedom (DOFs) planar RCM mechanism is proposed by constructing virtual parallelograms in this paper. The mechanism can be considered as a generalized double parallelogram; both of the actuated joints are revolute joints. This feature enhances the intrinsic back-drivability of the mechanism. The mathematical framework is introduced first to prove that the mechanism could execute RCM. Then, the inverse kinematics of the planar mechanism is solved, and the Jacobian matrix is derived in this paper. Further, the singularity and the kinematic performance based on the kinematic equations are investigated, and the workspace of the mechanism is verified. Finally, a prototype was built to test the function of the proposed RCM mechanism. The results show that the mechanism can fulfill the constraint of MIS, and it can be used as the basic element of the active manipulator in an MIS robot.

Background Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. Methods We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. Results The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. Conclusion The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work.

Human-machine interfaces (HMIs) based on electromyography (EMG) signals have been developed for simultaneous and proportional control (SPC) of multiple degrees of freedom (DoFs). The EMG-driven musculoskeletal model (MM) has been used in HMIs to predict human movements in prosthetic and robotic control. However, the neural information extracted from surface EMG signals may be distorted due to their limitations. With the development of high density (HD) EMG decomposition, accurate neural drive signals can be extracted from surface EMG signals. In this study, a neural-driven MM was proposed to predict metacarpophalangeal (MCP) joint flexion/extension and wrist joint flexion/extension. Ten able-bodied subjects (male) were recruited and tested. Four 64-channel electrode grids were attached to four forearm muscles of each subject to record the HD EMG signals. The joint angles were recorded synchronously. The acquired HD EMG signals were decomposed to extract the motor unit (MU) discharge for estimating the neural drive, which was then used as the input to the MM to calculate the muscle activation and predict the joint movements. The Pearson’s correlation coefficient (r) and the normalized root mean square error (NRMSE) between the predicted joint angles and the measured joint angles were calculated to quantify the estimation performance. Compared to the EMG-driven MM, the neural-driven MM attained higher r values and lower NRMSE values. Although the results were limited to an offline application and to a limited number of DoFs, they indicated that the neural-driven MM outperforms the EMG-driven MM in prediction accuracy and robustness. The proposed neural-driven MM for HMI can obtain more accurate neural commands and may have great potential for medical rehabilitation and robot control.