From the Dexterous Surgical Skill to the Battlefield—A Robotics Exploratory Study

Abstract

Introduction Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this article, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of the six main tasks of laparoscopic training. In addition, we provide a machine learning framework to evaluate novel transfer learning methodologies on this database. Methods A set of surgical gestures was collected for a peg transfer task, composed of seven atomic maneuvers referred to as surgemes. The collected Dexterous Surgical Skill dataset comprises a set of surgical robotic skills using the four robotic platforms: Taurus II, simulated Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different learning scenarios: no-transfer and domain-transfer. In the no-transfer scenario, the training and testing data were obtained from the same domain; whereas in the domain-transfer scenario, the training data are a blend of simulated and real robot data, which are tested on a real robot. Results Using simulation data to train the learning algorithms enhances the performance on the real robot where limited or no real data are available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-tosimulated data were 22% to 78%. For the Taurus II and the da Vinci, the model showed an accuracy of 97.5% and 93%, respectively, training only with simulation data. Conclusions The results indicate that simulation can be used to augment training data to enhance the performance of learned models in real scenarios. This shows potential for the future use of surgical data from the operating room in deployable surgical robots in remote areas.

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“…[83]. A training data set for automated surgical operations by robots and methods for its propagation and sharing to other robotic platforms were discussed in [84] in order to develop the capability to perform emergency medical/surgical procedures using AI to tackle the highly emergent situations where availability of healthcare professionals risks lives. Those surgical maneuvers were compiled as a library for sharing for continuous learning and improvements.…”

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“…It is important to know that every time a procedure is carried out with the da Vinci robotic system, the motion data of the robotic arms are being recorded and transmitted to the manufacturer. Engineers and computer scientists hope that the sheer quantity of these data will permit the generation of functioning algorithms that will ultimately result in more autonomous actions by surgical robots [71]. Although it is tempting to wonder whether or not this is possible, perhaps this is not the most important question.…”

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“…In the no-transfer scenario, the training and testing data were obtained from the same domain, compared to a domain-transfer scenario where training data is a blend of simulated and real robot data but tested on real robot data only. Authors in [19] tested this on several types of robots and found that the transfer model showed an accuracy of 81%, 97.5%, and 93% for the YuMi, Taurus II, and the da Vinci robot, respectively. While, in the YuMi case, the ratio of real-to-simulated data was 22% to 78%, later were trained only with simulation data.…”