Corrigendum: Deep Learning-Based Haptic Guidance for Surgical Skills Transfer.

[This corrects the article DOI: 10.3389/frobt.2020.586707.].

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer.

Having a trusted and useful system that helps to diminish the risk of medical errors and facilitate the improvement of quality in the medical education is indispensable. Thousands of surgical errors are occurred annually with high adverse event rate, despite inordinate number of devised patients safety initiatives. Inadvertently or otherwise, surgeons play a critical role in the aforementioned errors. Training surgeons is one of the most crucial and delicate parts of medical education and needs more attention due to its practical intrinsic. In contrast to engineering, dealing with mortal alive creatures provides a minuscule chance of trial and error for trainees. Training in operative rooms, on the other hand, is extremely expensive in terms of not only equipment but also hiring professional trainers. In addition, the COVID-19 pandemic has caused to establish initiatives such as social distancing in order to mitigate the rate of outbreak. This leads surgeons to postpone some non-urgent surgeries or operate with restrictions in terms of safety. Subsequently, educational systems are affected by the limitations due to the pandemic. Skill transfer systems in cooperation with a virtual training environment is thought as a solution to address aforesaid issues. This enables not only novice surgeons to enrich their proficiency but also helps expert surgeons to be supervised during the operation. This paper focuses on devising a solution based on deep leaning algorithms to model the behavior of experts during the operation. In other words, the proposed solution is a skill transfer method that learns professional demonstrations using different effective factors from the body of experts. The trained model then provides a real-time haptic guidance signal for either instructing trainees or supervising expert surgeons. A simulation is utilized to emulate an operating room for femur drilling surgery, which is a common invasive treatment for osteoporosis. This helps us with both collecting the essential data and assessing the obtained models. Experimental results show that the proposed method is capable of emitting guidance force haptic signal with an acceptable error rate.

LigLAP: Encirclement and Ligation of Vessels in Laparoscopic Surgery: A Double-Layer Suture Sealing Approach.

This article proposes a potential automatic ligation (LigLAP) method to occlude vessels and ducts in several laparoscopic surgical procedures. Currently, stapling devices are widely used for this purpose. However, there are some complications associated with stapling devices, including biliary leak and tissue damage. In this article, we examine the feasibility of an alternative method that uses a double-layer suture to encircle and occlude a vessel. A heating element melts the outer layer of the suture at the cross-point of the suture to create a seal. Several electromechanical mechanisms have been proposed to carry out this ligation process. In addition, some parts have been prototyped for experimental verification and visualization. Several double-layered sutures have been created, and their tensile strength and sealing capabilities have been measured. Moreover, a simple leakage experiment has been performed to verify experimentally the idea of using the double-layer suture. The results show that the new suture and the thermal sealing method provide the required strength to occlude balloons filled with water. Although the results suggest that the proposed method and the double-layer suture may be used in surgical ligation processes, much more rigorous testing of leakage is required.

Providing haptic feedback in robot-assisted minimally invasive surgery: a direct optical force-sensing solution for haptic rendering of deformable bodies.

This paper presents an enhanced haptic-enabled master-slave teleoperation system which can be used to provide force feedback to surgeons in minimally invasive surgery (MIS). One of the research goals was to develop a combined-control architecture framework that included both direct force reflection (DFR) and position-error-based (PEB) control strategies. To achieve this goal, it was essential to measure accurately the direct contact forces between deformable bodies and a robotic tool tip. To measure the forces at a surgical tool tip and enhance the performance of the teleoperation system, an optical force sensor was designed, prototyped, and added to a robot manipulator. The enhanced teleoperation architecture was formulated by developing mathematical models for the optical force sensor, the extended slave robot manipulator, and the combined-control strategy. Human factor studies were also conducted to (a) examine experimentally the performance of the enhanced teleoperation system with the optical force sensor, and (b) study human haptic perception during the identification of remote object deformability. The first experiment was carried out to discriminate deformability of objects when human subjects were in direct contact with deformable objects by means of a laparoscopic tool. The control parameters were then tuned based on the results of this experiment using a gain-scheduling method. The second experiment was conducted to study the effectiveness of the force feedback provided through the enhanced teleoperation system. The results show that the force feedback increased the ability of subjects to correctly identify materials of different deformable types. In addition, the virtual force feedback provided by the teleoperation system comes close to the real force feedback experienced in direct MIS. The experimental results provide design guidelines for choosing and validating the control architecture and the optical force sensor.