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Browsing by Author "Levy, Jeffrey S."
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Item Artificial Intelligence Methods and Artificial Intelligence-Enabled Metrics for Surgical Education: A Multidisciplinary Consensus(Wolters Kluwer, 2022) Vedula, S. Swaroop; Ghazi, Ahmed; Collins, Justin W.; Pugh, Carla; Stefanidis, Dimitrios; Meireles, Ozanan; Hung, Andrew J.; Schwaitzberg, Steven; Levy, Jeffrey S.; Sachdeva, Ajit K.; Collaborative for Advanced Assessment of Robotic Surgical Skills; Surgery, School of MedicineBackground: Artificial intelligence (AI) methods and AI-enabled metrics hold tremendous potential to advance surgical education. Our objective was to generate consensus guidance on specific needs for AI methods and AI-enabled metrics for surgical education. Study design: The study included a systematic literature search, a virtual conference, and a 3-round Delphi survey of 40 representative multidisciplinary stakeholders with domain expertise selected through purposeful sampling. The accelerated Delphi process was completed within 10 days. The survey covered overall utility, anticipated future (10-year time horizon), and applications for surgical training, assessment, and feedback. Consensus was agreement among 80% or more respondents. We coded survey questions into 11 themes and descriptively analyzed the responses. Results: The respondents included surgeons (40%), engineers (15%), affiliates of industry (27.5%), professional societies (7.5%), regulatory agencies (7.5%), and a lawyer (2.5%). The survey included 155 questions; consensus was achieved on 136 (87.7%). The panel listed 6 deliverables each for AI-enhanced learning curve analytics and surgical skill assessment. For feedback, the panel identified 10 priority deliverables spanning 2-year (n = 2), 5-year (n = 4), and 10-year (n = 4) timeframes. Within 2 years, the panel expects development of methods to recognize anatomy in images of the surgical field and to provide surgeons with performance feedback immediately after an operation. The panel also identified 5 essential that should be included in operative performance reports for surgeons. Conclusions: The Delphi panel consensus provides a specific, bold, and forward-looking roadmap for AI methods and AI-enabled metrics for surgical education.Item Demonstrating the effectiveness of the fundamentals of robotic surgery (FRS) curriculum on the RobotiX Mentor Virtual Reality Simulation Platform(Springer, 2021-04) Martin, John Rhodes; Stefanidis, Dimitrios; Dorin, Ryan P.; Goh, Alvin C.; Satava, Richard M.; Levy, Jeffrey S.; Surgery, School of MedicineFundamentals of robotic surgery (FRS) is a proficiency-based progression curriculum developed by robotic surgery experts from multiple specialty areas to address gaps in existing robotic surgery training curricula. The RobotiX Mentor is a virtual reality training platform for robotic surgery. Our aims were to determine if robotic surgery novices would demonstrate improved technical skills after completing FRS training on the RobotiX Mentor, and to compare the effectiveness of FRS across training platforms. An observational, pre-post design, multi-institutional rater-blinded trial was conducted at two American College of Surgeons Accredited Education Institutes-certified simulation centers. Robotic surgery novices (n = 20) were enrolled and trained to expert-derived benchmarks using FRS on the RobotiX Mentor. Participants’ baseline skill was assessed before (pre-test) and after (post-test) training on an avian tissue model. Tests were video recorded and graded by blinded raters using the Global Evaluative Assessment of Robotic Skills (GEARS) and a 32-criteria psychomotor checklist. Post hoc comparisons were conducted against previously published comparator groups. On paired-samples T tests, participants demonstrated improved performance across all GEARS domains (p < 0.001 to p = 0.01) and for time (p < 0.001) and errors (p = 0.003) as measured by psychometric checklist. By ANOVA, improvement in novices’ skill after FRS training on the RobotiX Mentor was not inferior to improvement reported after FRS training on previously published platforms. Completion of FRS on the RobotiX Mentor resulted in improved robotic surgery skills among novices, proving effectiveness of training. These data provide additional validity evidence for FRS and support use of the RobotiX Mentor for robotic surgery skill acquisition.Item Expert Consensus Recommendations for Robotic Surgery Credentialing(Wolters Kluwer, 2020-11) Stefanidis, Dimitrios; Huffman, Elizabeth M.; Collins, Justin W.; Martino, Martin A.; Satava, Richard M.; Levy, Jeffrey S.; Surgery, School of MedicineObjective: To define criteria for robotic credentialing using expert consensus. Background: A recent review of institutional robotic credentialing policies identified significant variability and determined current policies are largely inadequate to ensure surgeon proficiency and may threaten patient safety. Methods: 28 national robotic surgery experts were invited to participate in a consensus conference. After review of available institutional policies and discussion, the group developed a 91 proposed criteria. Using a modified Delphi process the experts were asked to indicate their agreement with the proposed criteria in three electronic survey rounds after the conference. Criteria that achieved 80% or more in agreement (consensus) in all rounds were included in the final list. Results: All experts agreed that there is a need for standardized robotic surgery credentialing criteria across institutions that promote surgeon proficiency. 49 items reached consensus in the first round, 19 in the second, and 8 in the third for a total of 76 final items. Experts agreed that privileges should be granted based on video review of surgical performance and attainment of clearly defined objective proficiency benchmarks. Parameters for ongoing outcome monitoring were determined and recommendations for technical skills training, proctoring, and performance assessment were defined. Conclusions: Using a systematic approach, detailed credentialing criteria for robotic surgery were defined. Implementation of these criteria uniformly across institutions will promote proficiency of robotic surgeons and has the potential to positively impact patient outcomes.Item How Wearable Technology Can Facilitate AI Analysis of Surgical Videos(Wolters Kluwer, 2020-10-05) Pugh, Carla M.; Ghazi, Ahmed; Stefanidis, Dimitrios; Schwaitzberg, Steven D.; Martino, Martin A.; Levy, Jeffrey S.; Surgery, School of MedicineOperative video has great potential to enable instant replays of critical surgical decisions for training and quality review. Recently, artificial intelligence (AI) has shown early promise as a method of enabling efficient video review, analysis, and segmentation. Despite the progress with AI analysis of surgical videos, more work needs to be done to improve the accuracy and efficiency of AI-driven video analysis. At a recent consensus conference held on July 10–11, 2020, 8 research teams shared their work using AI for surgical video analysis. Four of the teams showcased the utility of wearable technology in providing objective surgical metrics. Data from these technologies were shown to pinpoint important cognitive and motor actions during operative tasks and procedures. The results support the utility of wearable technology to facilitate efficient and accurate video analysis and segmentation.Item Proving the Effectiveness of the Fundamentals of Robotic Surgery (FRS) Skills Curriculum: A Single-blinded, Multispecialty, Multi-institutional Randomized Control Trial(Lippincott, 2020-08) Satava, Richard M.; Stefanidis, Dimitrios; Levy, Jeffrey S.; Smith, Roger; Martin, John R.; Monfared, Sara; Timsina, Lava R.; Wardkes Darzi, Ara; Moglia, Andrea; Brand, Timothy C.; Dorin, Ryan P.; Dumon, Kristoffel R.; Francone, Todd D.; Georgiou, Evangelos; Goh, Alvin C.; Marcet, Jorge E.; Martino, Martin A.; Sudan, Ranjan; Vale, Justin; Gallagher, Anthony G.; Surgery, School of MedicineObjective: To demonstrate the noninferiority of the fundamentals of robotic surgery (FRS) skills curriculum over current training paradigms and identify an ideal training platform. Summary Background Data: There is currently no validated, uniformly accepted curriculum for training in robotic surgery skills. Methods: Single-blinded parallel-group randomized trial at 12 international American College of Surgeons (ACS) Accredited Education Institutes (AEI). Thirty-three robotic surgery experts and 123 inexperienced surgical trainees were enrolled between April 2015 and November 2016. Benchmarks (proficiency levels) on the 7 FRS Dome tasks were established based on expert performance. Participants were then randomly assigned to 4 training groups: Dome (n = 29), dV-Trainer (n = 30), and DVSS (n = 32) that trained to benchmarks and control (n = 32) that trained using locally available robotic skills curricula. The primary outcome was participant performance after training based on task errors and duration on 5 basic robotic tasks (knot tying, continuous suturing, cutting, dissection, and vessel coagulation) using an avian tissue model (transfer-test). Secondary outcomes included cognitive test scores, GEARS ratings, and robot familiarity checklist scores. Results: All groups demonstrated significant performance improvement after skills training (P < 0.01). Participating residents and fellows performed tasks faster (DOME and DVSS groups) and with fewer errors than controls (DOME group; P < 0.01). Inter-rater reliability was high for the checklist scores (0.82–0.97) but moderate for GEARS ratings (0.40–0.67). Conclusions: We provide evidence of effectiveness for the FRS curriculum by demonstrating better performance of those trained following FRS compared with controls on a transfer test. We therefore argue for its implementation across training programs before surgeons apply these skills clinically.Item Response to “Proving the Effectiveness of the Fundamentals of Robotic Surgery (FRS) Skills Curriculum A Single-blinded, Multispecialty, Multi-institutional Randomized Control Trial” Not only surgeon's manual skills...”(Wolters Kluwer, 2020-12) Satava, Richard M.; Stefanidis, Dimitrios; Levy, Jeffrey S.; Smith, Roger; Martin, John R.; Monfared, Sara; Timsina, Lava R.; Wardkes Darzi, Ara; Moglia, Andrea; Brand, Timothy C.; Dorin, Ryan P.; Dumon, Kristoffel R.; Francone, Todd D.; Georgiou, Evangelos; Goh, Alvin C.; Marcet, Jorge E.; Martino, Martin A.; Sudan, Ranjan; Vale, Justin; Gallagher, Anthony G.; Surgery, School of Medicine