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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. While this is possible in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this paper, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of 6 main tasks of laparoscopic training. Also, we provide a ML framework to evaluate novel transfer learning methodologies on this database. The collected DESK 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 is a blend of simulated and real robot data that is tested on a real robot. Using simulation data enhances the performance of the real robot where limited or no real data is available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-to-simulated data was 22%-78%. For Taurus II and da Vinci robots, the model showed an accuracy of 97.5% and 93% respectively, training only with simulation data. Results indicate that simulation can be used to augment training data to enhance the performance of models in real scenarios. This shows the potential for future use of surgical data from the operating room in deployable surgical robots in remote areas.
Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semi-autonomous surgeries, skill assessment, and training. Simulated surgical environments
Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, the applicati
Surgical robots have had clinical use since the mid 1990s. Robot-assisted surgeries offer many benefits over the conventional approach including lower risk of infection and blood loss, shorter recovery, and an overall safer procedure for patients. Th
Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next dec
Miniaturized instruments are highly needed for robot assisted medical healthcare and treatment, especially for less invasive surgery as it empowers more flexible access to restricted anatomic intervention. But the robotic design is more challenging d