Journal of Gastric Cancer

The Korean Gastric Cancer Association (대한위암학회)
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Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

  • Received : 2023.07.15
  • Accepted : 2023.07.20
  • Published : 2023.07.31
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Abstract

Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.

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References

  1. Kitaguchi D, Takeshita N, Hasegawa H, Ito M. Artificial intelligence-based computer vision in surgery: recent advances and future perspectives. Ann Gastroenterol Surg 2022;6:29-36. https://doi.org/10.1002/ags3.12513
  2. Kitaguchi D, Watanabe Y, Madani A, Hashimoto DA, Meireles OR, Takeshita N, et al. Artificial intelligence for computer vision in surgery: a call for developing reporting guidelines. Ann Surg 2022;275:e609-e611.  https://doi.org/10.1097/SLA.0000000000005319
  3. Igaki T, Takenaka S, Watanabe Y, Kojima S, Nakajima K, Takabe Y, et al. Universal meta-competencies of operative performances: a literature review and qualitative synthesis. Surg Endosc 2023;37:835-845. https://doi.org/10.1007/s00464-022-09573-4
  4. Shinohara H. Surgery utilizing artificial intelligence technology: why we should not rule it out. Surg Today 2022;2022:3.
  5. Lalys F, Jannin P. Surgical process modelling: a review. Int J CARS 2014;9:495-511. https://doi.org/10.1007/s11548-013-0940-5
  6. Pernek I, Ferscha A. A survey of context recognition in surgery. Med Biol Eng Comput 2017;55:1719-1734. https://doi.org/10.1007/s11517-017-1670-6
  7. Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Artificial intelligence and surgical decision-making. JAMA Surg 2020;155:148-158. https://doi.org/10.1001/jamasurg.2019.4917
  8. Bamba Y, Ogawa S, Itabashi M, Kameoka S, Okamoto T, Yamamoto M. Automated recognition of objects and types of forceps in surgical images using deep learning. Sci Rep 2021;11:22571.
  9. Kitaguchi D, Fujino T, Takeshita N, Hasegawa H, Mori K, Ito M. Limited generalizability of single deep neural network for surgical instrument segmentation in different surgical environments. Sci Rep 2022;12:12575.
  10. Madad Zadeh S, Francois T, Calvet L, Chauvet P, Canis M, Bartoli A, et al. SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology. Surg Endosc 2020;34:5377-5383. https://doi.org/10.1007/s00464-019-07330-8
  11. Yamazaki Y, Kanaji S, Matsuda T, Oshikiri T, Nakamura T, Suzuki S, 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. https://doi.org/10.1016/j.jamcollsurg.2020.01.037
  12. Yamazaki Y, Kanaji S, Kudo T, Takiguchi G, Urakawa N, Hasegawa H, et al. Quantitative comparison of surgical device usage in laparoscopic gastrectomy between surgeons' skill levels: an automated analysis using a neural network. J Gastrointest Surg 2022;26:1006-1014. https://doi.org/10.1007/s11605-021-05161-4
  13. Kitaguchi D, Takeshita N, Matsuzaki H, Igaki T, Hasegawa H, Kojima S, et al. Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study. Surg Endosc 2022;36:6105-6112. https://doi.org/10.1007/s00464-022-09384-7
  14. Igaki T, Kitaguchi D, Kojima S, Hasegawa H, Takeshita N, Mori K, et al. Artificial intelligence-based total mesorectal excision plane navigation in laparoscopic colorectal surgery. Dis Colon Rectum 2022;65:e329-e333. https://doi.org/10.1097/DCR.0000000000002393
  15. Sato K, Fujita T, Matsuzaki H, Takeshita N, Fujiwara H, Mitsunaga S, et al. Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence. Surg Endosc 2022;36:5531-5539. https://doi.org/10.1007/s00464-022-09268-w
  16. Park SH, Park HM, Baek KR, Ahn HM, Lee IY, Son GM. Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol 2020;26:6945-6962. https://doi.org/10.3748/wjg.v26.i44.6945
  17. Barash Y, Klang E, Lux A, Konen E, Horesh N, Pery R, et al. Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonography. Langenbecks Arch Surg 2022;407:3553-3560. https://doi.org/10.1007/s00423-022-02674-7
  18. Kumazu Y, Kobayashi N, Kitamura N, Rayan E, Neculoiu P, Misumi T, et al. Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy. Sci Rep 2021;11:21198.
  19. Shinozuka K, Turuda S, Fujinaga A, Nakanuma H, Kawamura M, Matsunobu Y, et al. Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy. Surg Endosc 2022;36:7444-7452. https://doi.org/10.1007/s00464-022-09160-7
  20. Cheng K, You J, Wu S, Chen Z, Zhou Z, Guan J, et al. Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis. Surg Endosc 2022;36:3160-3168. https://doi.org/10.1007/s00464-021-08619-3
  21. Golany T, Aides A, Freedman D, Rabani N, Liu Y, Rivlin E, et al. Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy. Surg Endosc 2022;36:9215-9223. https://doi.org/10.1007/s00464-022-09405-5
  22. Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, et al. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc 2020;34:4924-4931. https://doi.org/10.1007/s00464-019-07281-0
  23. Kitaguchi D, Takeshita N, Matsuzaki H, Oda T, Watanabe M, Mori K, et al. Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: experimental research. Int J Surg 2020;79:88-94. https://doi.org/10.1016/j.ijsu.2020.05.015
  24. Kitaguchi D, Takeshita N, Matsuzaki H, Hasegawa H, Igaki T, Oda T, et al. Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision. Surg Endosc 2022;36:1143-1151. https://doi.org/10.1007/s00464-021-08381-6
  25. Sasaki K, Ito M, Kobayashi S, Kitaguchi D, Matsuzaki H, Kudo M, et al. Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: experimental research. Int J Surg 2022;105:106856.
  26. Takeuchi M, Kawakubo H, Saito K, Maeda Y, Matsuda S, Fukuda K, et al. Automated surgical-phase recognition for robot-assisted minimally invasive esophagectomy using artificial intelligence. Ann Surg Oncol 2022;29:6847-6855. https://doi.org/10.1245/s10434-022-11996-1
  27. Takeuchi M, Collins T, Ndagijimana A, Kawakubo H, Kitagawa Y, Marescaux J, et al. Automatic surgical phase recognition in laparoscopic inguinal hernia repair with artificial intelligence. Hernia 2022;26:1669-1678. https://doi.org/10.1007/s10029-022-02621-x
  28. Ward TM, Hashimoto DA, Ban Y, Rattner DW, Inoue H, Lillemoe KD, et al. Automated operative phase identification in peroral endoscopic myotomy. Surg Endosc 2021;35:4008-4015. https://doi.org/10.1007/s00464-020-07833-9
  29. Hashimoto DA, Rosman G, Witkowski ER, Stafford C, Navarette-Welton AJ, Rattner DW, et al. Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg 2019;270:414-421. https://doi.org/10.1097/SLA.0000000000003460
  30. Eckhoff JA, Ban Y, Rosman G, Muller DT, Hashimoto DA, Witkowski E, et al. TEsoNet: knowledge transfer in surgical phase recognition from laparoscopic sleeve gastrectomy to the laparoscopic part of Ivor-Lewis esophagectomy. Surg Endosc 2023;37:4040-4053. https://doi.org/10.1007/s00464-023-09971-2
  31. Fujinaga A, Endo Y, Etoh T, Kawamura M, Nakanuma H, Kawasaki T, et al. Development of a cross-artificial intelligence system for identifying intraoperative anatomical landmarks and surgical phases during laparoscopic cholecystectomy. Surg Endosc 2023;37:6118-6128. https://doi.org/10.1007/s00464-023-10097-8
  32. Takeuchi M, Kawakubo H, Tsuji T, Maeda Y, Matsuda S, Fukuda K, et al. Evaluation of surgical complexity by automated surgical process recognition in robotic distal gastrectomy using artificial intelligence. Surg Endosc 2023;37:4517-4524.
  33. Kowalewski KF, Garrow CR, Schmidt MW, Benner L, Muller-Stich BP, Nickel F. Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying. Surg Endosc 2019;33:3732-3740. https://doi.org/10.1007/s00464-019-06667-4
  34. Fard MJ, Ameri S, Darin Ellis R, Chinnam RB, Pandya AK, Klein MD. Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int J Med Robot 2018;14:e1850.
  35. Igaki T, Kitaguchi D, Matsuzaki H, Nakajima K, Kojima S, Hasegawa H, et al. Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence. JAMA Surg. Forthcoming 2023.
  36. Kitaguchi D, Takeshita N, Matsuzaki H, Igaki T, Hasegawa H, Ito M. Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis. JAMA Netw Open 2021;4:e2120786.
  37. Kitaguchi D, Teramura K, Matsuzaki H, Hasegawa H, Takeshita N, Ito M. Automatic purse-string suture skill assessment in transanal total mesorectal excision using deep learning-based video analysis. BJS Open 2023;7:zrac176.
  38. Sasaki S, Kitaguchi D, Takenaka S, Nakajima K, Sasaki K, Ogane T, et al. Machine learning-based automatic evaluation of tissue handling skills in laparoscopic colorectal surgery: a retrospective experimental study. Ann Surg 2023;278:e250-e255.
  39. Wang Z, Majewicz Fey A. Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J CARS 2018;13:1959-1970. https://doi.org/10.1007/s11548-018-1860-1