REFERENCES

1. 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.

2. Volkov M, Hashimoto DA, Rosman G, Meireles OR, Rus D. Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery. Proc - IEEE Int Conf Robot Autom 2017:p. 754-59.

3. Gupta N, Ranjan G, Arora MP, et al. Validation of a scoring system to predict difficult laparoscopic cholecystectomy. Int J Surg 2013;11:1002-6.

4. Madni TD, Leshikar DE, Minshall CT, et al. The Parkland grading scale for cholecystitis. The American Journal of Surgery 2018;215:625-30.

5. Wennmacker SZ, Bhimani N, van Dijk AH, Hugh TJ, de Reuver PR. Predicting operative difficulty of laparoscopic cholecystectomy in patients with acute biliary presentations. ANZ J Surg 2019;89:1451-6.

6. Griffiths EA, Hodson J, Vohra RS, et al. West Midlands Research Collaborative. Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy. Surg Endosc 2019;33:110-21.

7. Hugh TB, Chen FC, Hugh TJ, Li B. Laparoscopic cholecystectomy. A prospective study of outcome in 100 unselected patients. Med J Aust 1992;156:318-20.

8. Birkmeyer JD, Finks JF, O'Reilly A, et al. Michigan bariatric surgery collaborative. surgical skill and complication rates after bariatric surgery. N Engl J Med 2013;369:1434-42.

9. Zisimopoulos O, Flouty E, Luengo I, et al. DeepPhase: surgical phase recognition in CATARACTS videos. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-lópez C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention - MICCAI 2018. Cham: Springer International Publishing; 2018. p. 265-72.

10. Kadkhodamohammadi A, Sivanesan Uthraraj N, Giataganas P, et al. Towards video-based surgical workflow understanding in open orthopaedic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2021;9:286-93.

11. 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.

12. Fried G, Neville A. Mastery of endoscopic and laparoscopic surgery. 4th ed. (Lee L. Swanström NJS, ed.). Lippincott Williams and Wilkens; 2014.

13. Pignata G, Bracale U, Lazzara F. Laparoscopic surgery: key points, operating room setup and equipment. Springer; 2016.

14. Ellison CE, Zollinger RM. Zollinger’s Atlas of surgical operations.

15. Bonjer JH. Surgical principals of minimally invasive procedures: manual of the European association of endoscopic surgery. Available from: https://link.springer.com/book/10.1007/978-3-319-43196- [Last accessed on 23 Mar 2022]

16. Pucher PH, Brunt LM, Fanelli RD, Asbun HJ, Aggarwal R. SAGES expert Delphi consensus: critical factors for safe surgical practice in laparoscopic cholecystectomy. Surg Endosc 2015;29:3074-85.

17. Deal SB, Stefanidis D, Telem D, et al. Evaluation of crowd-sourced assessment of the critical view of safety in laparoscopic cholecystectomy. Surg Endosc 2017;31:5094-100.

18. O'Neill RS, Wennmacker SZ, Bhimani N, van Dijk AH, de Reuver P, Hugh TJ. Unsuspected choledocholithiasis found by routine intra-operative cholangiography during laparoscopic cholecystectomy. ANZ J Surg 2020;90:2279-84.

19. Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1. 0 Contributors. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020;17:261-72.

20. Vallat R. Pingouin: statistics in Python. J Open Source Softw 2018;3:1026.

21. Schneider DF, Mazeh H, Oltmann SC, Chen H, Sippel RS. Novel thyroidectomy difficulty scale correlates with operative times. World J Surg 2014;38:1984-9.

22. Tseng JF, Pisters PW, Lee JE, et al. The learning curve in pancreatic surgery. Surgery 2007;141:456-63.

23. Bourgouin S, Mancini J, Monchal T, Calvary R, Bordes J, Balandraud P. How to predict difficult laparoscopic cholecystectomy? Am J Surg 2016;212:873-81.

24. Cheng K, You J, Wu S, et al. Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis. Surg Endosc 2021; doi: 10.1007/s00464-021-08619-3.

25. Garrow CR, Kowalewski KF, Li L, et al. Machine learning for surgical phase recognition: a systematic review. Ann Surg 2021;273:684-93.

26. Wakabayashi G, Iwashita Y, Hibi T, et al. Tokyo guidelines 2018: surgical management of acute cholecystitis: safe steps in laparoscopic cholecystectomy for acute cholecystitis (with videos). J Hepatobiliary Pancreat Sci 2018;25:73-86.

27. Nijssen MA, Schreinemakers JM, Meyer Z, van der Schelling GP, Crolla RM, Rijken AM. Complications after laparoscopic cholecystectomy: a video evaluation study of whether the critical view of safety was reached. World J Surg 2015;39:1798-803.

28. Sebastian M, Sebastian A, Rudnicki J. Recommendation for photographic documentation of safe laparoscopic cholecystectomy. World J Surg 2021;45:81-7.

29. Mascagni P, Fiorillo C, Urade T, et al. Formalizing video documentation of the critical view of safety in laparoscopic cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety. Surg Endosc 2020;34:2709-14.

30. Greenberg JA, Minter RM. Entrustable professional activities: the future of competency-based education in surgery may already be here. Ann Surg 2019;269:407-8.

31. Knox ADC, Gilardino MS, Kasten SJ, Warren RJ, Anastakis DJ. Competency-based medical education for plastic surgery: where do we begin? Plast Reconstr Surg 2014;133:702e-10e.

32. Nousiainen MT, Mironova P, Hynes M, et al. CBC Planning Committee. Eight-year outcomes of a competency-based residency training program in orthopedic surgery. Med Teach 2018;40:1042-54.

33. Kundu S. AI in medicine must be explainable. Nat Med 2021;27:1328.

34. Repici A, Badalamenti M, Maselli R, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 2020;159:512-520.e7.

35. Repici A, Spadaccini M, Antonelli G, et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2022;71:757-65.

36. 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 2020; doi: 10.1097/SLA.0000000000004594.

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
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