Page 61 - Read Online
P. 61
Page 464
Goyal et al. Art Int Surg. 2025;5:450-64 https://dx.doi.org/10.20517/ais.2024.108
13. Cui P, Zhao S, Chen W, Peng J. Identification of the vas deferens in laparoscopic inguinal hernia repair surgery using the
convolutional neural network. J Healthc Eng. 2021;2021:1-10. DOI PubMed PMC
14. Taha A, Enodien B, Frey DM, Taha-mehlitz S. The development of artificial intelligence in hernia surgery: a scoping review. Front
Surg. 2022;9:908014. DOI PubMed PMC
15. Elfanagely O, Mellia JA, Othman S, Basta MN, Mauch JT, Fischer JP. Computed tomography image analysis in abdominal wall
reconstruction: a systematic review. Plast Reconstr Surg Glob Open. 2020;8:e3307. DOI PubMed PMC
16. Elhage SA, Deerenberg EB, Ayuso SA, et al. Development and validation of image-based deep learning models to predict surgical
complexity and complications in abdominal wall reconstruction. JAMA Surg. 2021;156:933. DOI PubMed PMC
17. Niebuhr H, Born O. Image tracking system eine neue Technik für die sichere und kostensparende laparoskopische operation. Chirurg.
2000;71:580-4. DOI PubMed
18. Haddaway NR, Page MJ, Pritchard CC, Mcguinness LA. PRISMA2020: an R package and Shiny app for producing PRISMA
2020 compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst Rev.
2022;18:e1230. DOI PubMed PMC
19. Moher D, Shamseer L, Clarke M, et al. PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols
(PRISMA-P) 2015 statement. Syst Rev. 2015;4:1. DOI PubMed PMC
20. Takeuchi M, Collins T, Lipps C, et al. Towards automatic verification of the critical view of the myopectineal orifice with artificial
intelligence. Surg Endosc. 2023;37:4525-34. DOI PubMed
21. 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
22. Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med. 2020;3:126. DOI
PubMed PMC
23. Lima DL, Kasakewitch J, Nguyen DQ, et al. Machine learning, deep learning and hernia surgery. Are we pushing the limits of
abdominal core health? Hernia. 2024;28:1405-12. DOI PubMed
24. Gagandeep K, Rishabh M, Vyas S. Artificial intelligence (AI) startups in health sector in India: challenges and regulation in India. In:
Goyal D, Kumar A, Piuri V, Paprzycki M, Editors. Proceedings of the Third International Conference on Information Management and
Machine Intelligence; 2021 Dec 23-24; Jaipur, India. Singapore: Springer; 2023. pp. 203-15. DOI
25. Pharmaphorum. Sensely and Mayo Clinic take virtual nurse one step further. Available from: https://pharmaphorum.com/news/
sensely-mayo-clinic-develop-virtual-doctor. [Last accessed on 12 Sep 2025].
26. Pharmaphorum. FDA approves Caption Health’s AI-driven cardiac ultrasound software. Available from: https://pharmaphorum.com/
news/fda-approves-caption-healths-ai-driven-cardiac-imaging-software. [Last accessed on 12 Sep 2025].
27. 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
28. Zhang B, Ghanem A, Simes A, Choi H, Yoo A. Surgical workflow recognition with 3DCNN for sleeve gastrectomy. Int J Comput
Assist Radiol Surg. 2021;16:2029-36. DOI PubMed PMC
29. 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. 2019;34:4924-31. DOI PubMed
30. Ward TM, Hashimoto DA, Ban Y, et al. Automated operative phase identification in peroral endoscopic myotomy. Surg Endosc.
2020;35:4008-15. DOI PubMed PMC
31. 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. 2016;36:86-97. DOI PubMed
32. Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3:210-29. DOI
33. 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
34. Czempiel T, Paschali M, Keicher M, et al. TeCNO: surgical phase recognition with multi-stage temporal convolutional networks. In:
Martel AL, Abolmaesumi P, Stoyanov D, et al., Editors. Medical Image Computing and Computer Assisted Intervention - MICCAI
2020; 2020 Oct 4-8; Lima, Peru. Cham: Springe; 2020. pp. 343-52. DOI
35. Jin Y, Long Y, Chen C, Zhao Z, Dou Q, Heng PA. Temporal memory relation network for workflow recognition from surgical video.
IEEE Trans Med Imaging. 2021;40:1911-23. DOI PubMed
36. Gao X, Jin Y, Long Y, Dou Q, Heng PA. Trans-SVNet: accurate phase recognition from surgical videos via hybrid embedding
aggregation transformer. In: De Bruijne M, Cattin PC, Cotin S, et al., Editors. Medical Image Computing and Computer Assisted
Intervention - MICCAI 2021. 2021 Sep 27-Oct 1; Strasbourg, France. Cham: Springer; 2021. pp. 593-603. DOI
37. Park M, Oh S, Jeong T, Yu S. Multi-stage temporal convolutional network with moment loss and positional encoding for surgical
phase recognition. Diagnostics. 2022;13:107. DOI PubMed PMC

