Feasibility of expert and crowd-sourced review of intraoperative video for quality improvement of intracorporeal urinary diversion during robotic radical cystectomy

Goldenberg, Mitchell; Nabhani, Jamal; Wallis, Christopher J.D.; Chopra, Sameer; Hung, Andrew J.; Schuckman, Anne; Djaladat, Hooman; Daneshmand, Siamak; Desai, Mihir; Aron, Monish; Gill, Inderbir S.; Satkunasivam, Raj

doi:10.5489/cuaj.4442

Cited by 9 publications

(1 citation statement)

References 26 publications

(29 reference statements)

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“…One criticism of using crowdsourcing was that the scores and results were not generalizable to the evaluation of live surgery but rather only to dry laboratories [42]. This has also been demonstrated in the world of urology, where a study found an inability of crowdsourcing and video review to predict complications associated with robot-assisted radical cystectomy [44]. Other studies have shown that blinded video assessments of a surgeon's technique correlate with patient outcomes [45].…”

Section: Crowdsourcingmentioning

confidence: 99%

Robotic Surgery: The Impact of Simulation and Other Innovative Platforms on Performance and Training

Azadi

Green

Arnold

et al. 2021

Journal of Minimally Invasive Gynecology

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To review the current status of robotic training and the impact of various training platforms on the performance of robotic surgical trainees. Data Sources: Literature review of Google Scholar and PubMed. The search terms included a combination of the following: "robotic training," "simulation," "robotic curriculum," "obgyn residency robotic training," "virtual reality robotic training," "DaVinci training," "surgical simulation," "gyn surgical training." The sources considered for inclusion included peerreviewed articles, literature reviews, textbook chapters, and statements from various institutions involved in resident training. Methods of Study Selection: A literature search of Google Scholar and PubMed using terms related to robotic surgery and robotics training, as mentioned in the "Data Sources" section. Results: Multiple novel platforms that use machine learning and real-time video feedback to teach and evaluate robotic surgical skills have been developed over recent years. Various training curricula, virtual reality simulators, and other robotic training tools have been shown to enhance robotic surgical education and improve surgical skills. The integration of didactic learning, simulation, and intraoperative teaching into more comprehensive training curricula shows positive effects on robotic skills proficiency. Few robotic surgery training curricula have been validated through peer-reviewed study, and there is more work to be completed in this area. In addition, there is a lack of information about how the skills obtained through robotics curricula and simulation translate into operating room performance and patient outcomes. Conclusion: Data collected to date show promising advances in the training of robotic surgeons. A diverse array of curricula for training robotic surgeons continue to emerge, and existing teaching modalities are evolving to keep up with the rapidly growing demand for proficient robotic surgeons. Futures areas of growth include establishing competency benchmarks for existing training tools, validating existing curricula, and determining how to translate the acquired skills in simulation into performance in the operating room and patient outcomes. Many surgical training platforms are beginning to expand beyond discrete robotic skills training to procedure-specific and team training. There is still a wealth of research to be done to understand how to create an effective training experience for gynecologic surgical trainees and robotics teams.

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Section: Crowdsourcingmentioning

confidence: 99%