Page 31 - Read Online

Body et al. Art Int Surg 2022;2:186-94 https://dx.doi.org/10.20517/ais.2022.28 Page 194

pancreaticoduodenectomy across low and high volume centers. Ann Surg 2019;270:1147-55. DOI PubMed
43. Ceccarelli G, Andolfi E, Fontani A, Calise F, Rocca A, Giuliani A. Robot-assisted liver surgery in a general surgery unit with a
“Referral Centre Hub&Spoke Learning Program”. Early outcomes after our first 70 consecutive patients. Minerva Chir 2018;73:460-8.
DOI PubMed
44. Gall TMH, Alrawashdeh W, Soomro N, White S, Jiao LR. Shortening surgical training through robotics: randomized clinical trial of
laparoscopic versus robotic surgical learning curves. BJS Open 2020;4:1100-8. DOI PubMed PMC
45. Zhang T, Zhao ZM, Gao YX, Lau WY, Liu R. The learning curve for a surgeon in robot-assisted laparoscopic
pancreaticoduodenectomy: a retrospective study in a high-volume pancreatic center. Surg Endosc 2019;33:2927-33. DOI PubMed
46. Lu C, Jin W, Mou YP, et al. Analysis of learning curve for laparoscopic pancreaticoduodenectomy. J Vis Surg 2016;2:145. DOI
PubMed PMC
47. Gall TMH, Malhotra G, Elliott JA, Conneely JB, Fong Y, Jiao LR. The Atlantic divide: contrasting surgical robotics training in the
USA, UK and Ireland. J Robot Surg 2022. DOI PubMed
48. Takagi K, Umeda Y, Yoshida R, et al. Surgical training model and safe implementation of robotic pancreatoduodenectomy in Japan: a
technical note. World J Surg Oncol 2021;19:55. DOI PubMed PMC
49. Rice MK, Hodges JC, Bellon J, et al. Association of mentorship and a formal robotic proficiency skills curriculum with subsequent
generations’ learning curve and safety for robotic pancreaticoduodenectomy. JAMA Surg 2020;155:607-15. DOI PubMed PMC
50. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights
Imaging 2018;9:611-29. DOI PubMed PMC
51. Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019;8:2328. DOI
PubMed PMC
52. Deo RC. Machine learning in medicine. Circulation 2015;132:1920-30. DOI PubMed PMC
53. Mascagni P, Alapatt D, Urade T, et al. A computer vision platform to automatically locate critical events in surgical videos:
documenting safety in laparoscopic cholecystectomy. Ann Surg 2021;274:e93-5. DOI PubMed
54. Malpani A, Vedula SS, Lin HC, Hager GD, Taylor RH. Effect of real-time virtual reality-based teaching cues on learning needle
passing for robot-assisted minimally invasive surgery: a randomized controlled trial. Int J Comput Assist Radiol Surg 2020;15:1187-
94. DOI PubMed
55. Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N. EndoNet: a deep architecture for recognition tasks on
laparoscopic videos. IEEE Trans Med Imaging 2017;36:86-97. DOI PubMed
56. Jin Y, Dou Q, Chen H, et al. SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network. IEEE
Trans Med Imaging 2018;37:1114-26. DOI PubMed
57. Hashimoto DA, Rosman G, Witkowski ER, et al. Computer vision analysis of intraoperative video: automated recognition of operative
steps in laparoscopic sleeve gastrectomy. Ann Surg 2019;270:414-21. DOI PubMed PMC
58. Morita S, Tabuchi H, Masumoto H, Yamauchi T, Kamiura N. Real-time extraction of important surgical phases in cataract surgery
videos. Sci Rep 2019;9:16590. DOI PubMed PMC
59. 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
60. Ward TM, Mascagni P, Ban Y, et al. Computer vision in surgery. Surgery 2021;169:1253-6. DOI PubMed
61. Kitaguchi D, Takeshita N, Matsuzaki H, et al. Automated laparoscopic colorectal surgery workflow recognition using artificial
intelligence: experimental research. Int J Surg 2020;79:88-94. DOI PubMed
62. Yamazaki Y, Kanaji S, Matsuda T, et al. Automated surgical instrument detection from laparoscopic gastrectomy video images using
an open source convolutional neural network platform. J Am Coll Surg 2020;230:725-732.e1. DOI PubMed
63. Lee D, Yu HW, Kwon H, Kong HJ, Lee KE, Kim HC. Evaluation of surgical skills during robotic surgery by deep learning-based
multiple surgical instrument tracking in training and actual operations. J Clin Med 2020;9:1964. DOI PubMed PMC
64. Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of deep learning models for identifying surgical actions and
measuring performance. JAMA Netw Open 2020;3:e201664. DOI PubMed
65. 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
66. Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify
surgical anatomy during laparoscopic cholecystectomy. Ann Surg 2022;276:363-9. DOI PubMed PMC
67. Mascagni P, Vardazaryan A, Alapatt D, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of
safety in laparoscopic cholecystectomy using deep learning. Ann Surg 2022;275:955-61. DOI PubMed
68. Stulberg JJ, Huang R, Kreutzer L, et al. Association between surgeon technical skills and patient outcomes. JAMA Surg 2020;155:960-
8. DOI PubMed PMC
69. Hung AJ, Chen J, Che Z, et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical
prostatectomy performance and predict outcomes. J Endourol 2018;32:438-44. DOI PubMed
70. Jarc AM, Curet MJ. Viewpoint matters: objective performance metrics for surgeon endoscope control during robot-assisted surgery.
Surg Endosc 2017;31:1192-202. DOI PubMed PMC