Simulation-Based Training in Robot-Assisted Surgery: Current Evidence of Value and Potential Trends for the Future

Hanzly, Michael; Al-Tartir, Tareq; Raza, Syed Johar; Khan, Atif; Durrani, Mohammad; Fiorica, Thomas; Ginsberg, Phillip C.; Mohler, James L.; Kuvshinoff, Boris W.; Guru, Khurshid A.

doi:10.1007/s11934-015-0508-8

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…Although the simulators used by our curriculum also represent a costly resource, they represent a fixed‐cost and therefore are more cost‐effective than devising laboratories with single‐use cadaver or animal models for robotic skill acquisitions. The validity of simulators in robotic training and the use of simulators for objective assessment are well supported, particularly within the urology literature . These robotic simulators, when costs are shared among the several robotic surgical specialties, can become a financially justifiable purchase.…”

Section: Discussionmentioning

confidence: 99%

Initial and Long‐term Retention of Robotic Technical Skills in an Otolaryngology Residency Program

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Initial and Long‐term Retention of Robotic Technical Skills in an Otolaryngology Residency Program

et al. 2018

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“…Analyses of adverse events have found error is non‐technical skills such as communication, leadership, situational awareness, decision‐making, and teamwork to be at the center of many events . Methods to provide instruction and assess these issues and assess the non‐technical skills are a growing area of research . Recently, crowdsourcing of recorded video has been developed to objectively assess technical skills as well as provide benchmarks and feedback.…”

Section: Introductionmentioning

confidence: 99%

Incorporating resident/fellow training into a robotic surgery program

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Green

Martino

et al. 2015

Journal of Surgical Oncology

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With the rapid uptake of the robotic approach in gynecologic surgery, a thorough understanding of the technology, including its uses and limitations, is critical to maximize patient outcomes and safety. This review discusses the role of training modalities and development of curricula for robotic surgery. Furthermore, methods for incorporating the entire surgical team and the process of credentialing/maintaining privileges are described.

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“…Robot-assisted surgery (RAS) is the latest advancement in clinical minimally invasive surgery (MIS). RAS has improved surgeons' dexterity, precision, and ergonomics, and has made complex surgical procedures easier compared to other types of MIS [ 1 , 2 ]. RAS has also contributed to improvements in surgical outcomes such as fewer peri- and postoperative complications in different surgical fields [ 3 , 4 ].…”

mentioning

confidence: 99%

Acquisition and usage of robotic surgical data for machine learning analysis

et al. 2023

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Background The increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment. Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting. Method We tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: ‘Capturing image data from the surgical robot’, ‘Extracting event data’, ‘Capturing movement data of the surgeon’, ‘Annotation of image data’. Results 15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons’ arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI. Conclusion With our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI.

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Simulation-Based Training in Robot-Assisted Surgery: Current Evidence of Value and Potential Trends for the Future

Cited by 10 publications

References 48 publications

Initial and Long‐term Retention of Robotic Technical Skills in an Otolaryngology Residency Program

Initial and Long‐term Retention of Robotic Technical Skills in an Otolaryngology Residency Program

Incorporating resident/fellow training into a robotic surgery program

Acquisition and usage of robotic surgical data for machine learning analysis

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