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Choksi et al. Art Int Surg. 2025;5:160-9  https://dx.doi.org/10.20517/ais.2024.84                                                           Page 169

               Financial support and sponsorship
               None.


               Conflicts of interest
               Filicori F is a paid consultant for Boston Scientific. The other authors declared that there are no conflicts of
               interest.

               Ethical approval and consent to participate
               This study was approved by the Institutional Review Board of Northwell Health (IRB 23-069). All
               participants signed a consent form to participate in the study.

               Consent for publication
               Not applicable.


               Copyright
               © The Author(s) 2025.


               REFERENCES
               1.       Kitaguchi D, Takeshita N, Matsuzaki H, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the
                   convolutional neural network-based deep learning approach. Surg Endosc. 2020;34:4924-31.  DOI  PubMed
               2.       Cai T, Zhao Z. Convolutional neural network-based surgical instrument detection. Technol Health Care. 2020;28:81-8.  DOI  PubMed
                   PMC
               3.       Luongo F, Hakim R, Nguyen JH, Anandkumar A, Hung AJ. Deep learning-based computer vision to recognize and classify suturing
                   gestures in robot-assisted surgery. Surgery. 2021;169:1240-4.  DOI  PubMed  PMC
               4.       Choksi S, Szot S, Zang C, et al. Bringing artificial intelligence to the operating room: edge computing for real-time surgical phase
                   recognition. Surg Endosc. 2023;37:8778-84.  DOI  PubMed
               5.       Birkmeyer JD, Finks JF, O’Reilly A, et al. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013;369:1434-
                   42.  DOI
               6.       Grewal B, Kianercy A, Gerrah R. Characterization of surgical movements as a training tool for improving efficiency. J Surg Res.
                   2024;296:411-7.  DOI  PubMed
               7.       Azari DP, Frasier LL, Quamme SRP, et al. Modeling surgical technical skill using expert assessment for automated computer rating.
                   Ann Surg. 2019;269:574-81.  DOI  PubMed  PMC
               8.       Welcome to FLS. Fundamentals of laparoscopic surgery. Available from: https://www.flsprogram.org/about-fls/. [Last accessed on 27
                   Mar 2025].
               9.       Fuchs HF, Collins JW, Babic B, et al. Robotic-assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer training
                   curriculum-a worldwide Delphi consensus study. Dis Esophagus. 2022;35:doab055.  DOI  PubMed
               10.      Stegemann AP, Ahmed K, Syed JR, et al. Fundamental skills of robotic surgery: a multi-institutional randomized controlled trial for
                   validation of a simulation-based curriculum. Urology. 2013;81:767-74.  DOI  PubMed
               11.      Satava RM, Stefanidis D, Levy JS, et al. Proving the effectiveness of the fundamentals of robotic surgery (FRS) skills curriculum: a
                   single-blinded, multispecialty, multi-institutional randomized control trial. Ann Surg. 2020;272:384-92.  DOI  PubMed
               12.      Ayoub-Charette S, McGlynn ND, Lee D, et al. Rationale, design and participants baseline characteristics of a crossover randomized
                   controlled trial of the effect of replacing SSBs with NSBs versus water on glucose tolerance, gut microbiome and cardiometabolic risk
                   in overweight or obese adult SSB consumer: strategies to oppose SUGARS with non-nutritive sweeteners or water (STOP sugars
                   NOW) trial and ectopic fat sub-study. Nutrients. 2023;15:1238.  DOI  PubMed  PMC
               13.      Lazar A, Sroka G, Laufer S. Automatic assessment of performance in the FLS trainer using computer vision. Surg Endosc.
                   2023;37:6476-82.  DOI  PubMed
               14.      Islam G, Kahol K, Li B, Smith M, Patel VL. Affordable, web-based surgical skill training and evaluation tool. J Biomed Inform.
                   2016;59:102-14.  DOI  PubMed
               15.      Hung AJ, Bao R, Sunmola IO, Huang DA, Nguyen JH, Anandkumar A. Capturing fine-grained details for video-based automation of
                   suturing skills assessment. Int J Comput Assist Radiol Surg. 2023;18:545-52.  DOI  PubMed  PMC
               16.      Ma R, Kiyasseh D, Laca JA, et al. Artificial intelligence-based video feedback to improve novice performance on robotic suturing
                   skills: a pilot study. J Endourol. 2024;38:884-91.  DOI  PubMed
               17.      Raza SJ, Field E, Jay C, et al. Surgical competency for urethrovesical anastomosis during robot-assisted radical prostatectomy:
                   development and validation of the robotic anastomosis competency evaluation. Urology. 2015;85:27-32.  DOI  PubMed
               18.      Otiato MX, Ma R, Chu TN, Wong EY, Wagner C, Hung AJ. Surgical gestures to evaluate apical dissection of robot-assisted radical
                   prostatectomy. J Robot Surg. 2024;18:245.  DOI  PubMed  PMC
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