5 Minute HealthTech Jargon Buster: Robot Assisted Surgery

The first FDA approved surgery robot is known as the ‘da Vinci Robot’ [2]. Cleared in 2000, it was rudimental in shaping the operations of robotic systems that are used today. The da Vinci is composed of a surgeon console, patient cart and a vision cart; each component playing an essential role in maximising surgery success and minimising surgery complication [3]. The patient cart comprises of robotic mechanical arms, with surgical equipment attached, that are to be used in surgery while the surgeon console is where the surgeon controls the robotic arms remotely. The vision cart enables the production of magnified 3D images of the surgical field which the surgeon can view and aids their control of the surgical instruments. The role of the surgeon while using the device is to puppeteer the robotic arms in surgery.

Another RAS device is the FDA-approved Cambridge Medical Robotics (CMR) Versius system which is designed around the human arm to perform cholecystectomies via soft tissue minimal access surgery [9]. A cholecystectomy is the surgical removal of the gall bladder and is a common treatment for gall stones. In comparison to the da Vinci system, the CMR Versius system has been found to have longer procedure times in inguinal and ventral hernia surgeries but not in cholecystectomies [10]. Nevertheless, no intra-operative complications occurred with either system, signifying the potential of the Versius system.

Advancements in technology have enabled the application of haptic feedback technology in RAS devices. This enables surgeons to be provided with haptic feedback while performing surgeries, providing them with a more realistic feel while operating. Surgeons can feel tissue texture and organ resistance while operating which can help to feel more connected to the tissue that is being operated on, likely improving accuracy and reducing surgical error [5].

Artificial Intelligence (AI) is used to optimise the robot assisted surgery process. It can provide user suggestions for subsequent actions in surgery, for example, and machine learning (ML) can be used to conduct extensive data analysis to enhance the performance of surgical procedures by automating different aspects of surgery. For instance, ML can assist with tissue identification as well as camera positioning. Machine learning uses kinematic data from surgical instruments, laparoscopic video recording and surgeon eye tracking to obtain autonomous camera positioning, whereby the camera adjusts itself to enhance magnified visualisation of the different areas of the surgical field [12].

ML is a type of artificial intelligence that enables systems to learn from data sets and recognise patterns to subsequently use this information to make predictions and inform future decisions. AI can be used to provide real time intraoperative feedback by video evaluation and analysis of robotic hand movements to provide automated performance metrics (APM). This can be used to predict patient outcomes by evaluating and providing feedback on a surgeon’s performance for improvement, which would also be useful in surgical training.

Despite its benefits, there are still some challenges for the implementation of robot assisted surgery into healthcare on a widespread scale. For example, the high procedure cost [2] associated with the use of these robots as well as the cost of the robots themselves, presents itself as a barrier to widespread implementation of RAS into healthcare systems. This is particularly exacerbated when considering access to the technology for the developing world and the negative impact on health equity.

In addition, use of the system requires healthcare professionals to undergo extensive and expensive training to be fully equipped to perform procedures using these robots which not all healthcare systems can undertake.

A challenge of the technology relates to the potential for complications during use. For instance, in a 2008 study on the use of robots in a laparoscopic prostatectomy in prostate cancer treatment, robot malfunction was found to occur in 0.4-4.6% of cases [7]. Another challenge relates to the size of RAS devices. Due to the variety of components that RAS devices comprise of; they can take up a large amount of space in operating rooms. With the already crowded operating rooms of today, this can pose as a limiting factor of its use and may either limit the number of healthcare personnel that can be present in operating rooms (ORs) or induce the need for larger ORs [13].