From the Dexterous Surgical Skill to the Battlefield-A Robotics Exploratory Study.
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...
| Published in: | Military Medicine Vol. 186; pp. 288 - 295 |
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| Main Authors: | , , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
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Oxford University Press / USA
2021 Supplement
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148634953&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148634953 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00264075 4DV jtl: Military Medicine issn: 00264075 maglogo: N pubinfo: dt: 2021 Supplement vid: 186 pid: 622 pub: Oxford University Press / USA artinfo: ui: 148634953 148634953 NLM33499518 148634953 10.1093/milmed/usaa253 NLM33499518 148634953 ppf: 288 ppct: 7 formats: fmt: @attributes: type: P tig: atl: From the Dexterous Surgical Skill to the Battlefield-A Robotics Exploratory Study. aug: au: Gonzalez, Glebys T. Kaur, Upinder Rahman, Masudur Venkatesh, Vishnunandan Sanchez, Natalia Hager, Gregory Yexiang Xue Voyles, Richard Wachs, Juan Xue, Yexiang affil: Department of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA. sug: subj: Robotics Human Clinical Competence Laparoscopy Computer Simulation Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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