Abstract
Background
Attention to anatomical landmarks in the appropriate surgical phase is important to prevent bile duct injury (BDI) during laparoscopic cholecystectomy (LC). Therefore, we created a cross-AI system that works with two different AI algorithms simultaneously, landmark detection and phase recognition. We assessed whether landmark detection was activated in the appropriate phase by phase recognition during LC and the potential contribution of the cross-AI system in preventing BDI through a clinical feasibility study (J-SUMMIT-C-02).
Methods
A prototype was designed to display landmarks during the preparation phase and Calot's triangle dissection. A prospective clinical feasibility study using the cross-AI system was performed in 20 LC cases. The primary endpoint of this study was the appropriateness of the detection timing of landmarks, which was assessed by an external evaluation committee (EEC). The secondary endpoint was the correctness of landmark detection and the contribution of cross-AI in preventing BDI, which were assessed based on the annotation and 4-point rubric questionnaire.
Results
Cross-AI-detected landmarks in 92% of the phases where the EEC considered landmarks necessary. In the questionnaire, each landmark detected by AI had high accuracy, especially the landmarks of the common bile duct and cystic duct, which were assessed at 3.78 and 3.67, respectively. In addition, the contribution to preventing BDI was relatively high at 3.65.
Conclusions
The cross-AI system provided landmark detection at appropriate situations. The surgeons who previewed the model suggested that the landmark information provided by the cross-AI system may be effective in preventing BDI. Therefore, it is suggested that our system could help prevent BDI in practice.
Trial registration University Hospital Medical Information Network Research Center Clinical Trial Registration System (UMIN000045731).
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Yu KH, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2:719–731
Anteby R, Horesh N, Soffer S, Zager Y, Barash Y, Amiel I, Rosin D, Gutman M, Klang E (2021) Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis. Surg Endosc 35:1521–1533
Shiroshita H, Inomata M, Akira S, Kanayama H, Yamaguchi S, Eguchi S, Wada N, Kurokawa Y, Uchida H, Seki Y, Ieiri S, Iwazaki M, Sato Y, Kitamura K, Tabata M, Mimata H, Takahashi H, Uemura T, Akagi T, Taniguchi F, Miyajima A, Hashizume M, Matsumoto S, Kitano S, Watanabe M, Sakai Y (2022) Current status of endoscopic surgery in Japan: the 15th national survey of endoscopic surgery by the Japan society for endoscopic surgery. Asian J Endosc Surg 15:415–426
Kitamura H, Fujioka S, Hata T, Misawa T, Yanaga K (2020) Segment IV approach for difficult laparoscopic cholecystectomy. Ann Gastroenterol Surg 4:170–174
Iwashita Y, Hibi T, Ohyama T, Umezawa A, Takada T, Strasberg SM, Asbun HJ, Pitt HA, Han HS, Hwang TL, Suzuki K, Yoon YS, Choi IS, Yoon DS, Huang WS, Yoshida M, Wakabayashi G, Miura F, Okamoto K, Endo I, de Santibañes E, Giménez ME, Windsor JA, Garden OJ, Gouma DJ, Cherqui D, Belli G, Dervenis C, Deziel DJ, Jonas E, Jagannath P, Supe AN, Singh H, Liau KH, Chen XP, Chan ACW, Lau WY, Fan ST, Chen MF, Kim MH, Honda G, Sugioka A, Asai K, Wada K, Mori Y, Higuchi R, Misawa T, Watanabe M, Matsumura N, Rikiyama T, Sata N, Kano N, Tokumura H, Kimura T, Kitano S, Inomata M, Hirata K, Sumiyama Y, Inui K, Yamamoto M (2017) Delphi consensus on bile duct injuries during laparoscopic cholecystectomy: an evolutionary cul-de-sac or the birth pangs of a new technical framework? J Hepatobiliary Pancreat Sci 24:591–602
Wakabayashi G, Iwashita Y, Hibi T, Takada T, Strasberg SM, Asbun HJ, Endo I, Umezawa A, Asai K, Suzuki K, Mori Y, Okamoto K, Pitt HA, Han HS, Hwang TL, Yoon YS, Yoon DS, Choi IS, Huang WS, Giménez ME, Garden OJ, Gouma DJ, Belli G, Dervenis C, Jagannath P, Chan ACW, Lau WY, Liu KH, Su CH, Misawa T, Nakamura M, Horiguchi A, Tagaya N, Fujioka S, Higuchi R, Shikata S, Noguchi Y, Ukai T, Yokoe M, Cherqui D, Honda G, Sugioka A, de Santibañes E, Supe AN, Tokumura H, Kimura T, Yoshida M, Mayumi T, Kitano S, Inomata M, Hirata K, Sumiyama Y, Inui K, Yamamoto M (2018) Tokyo Guidelines 2018: surgical management of acute cholecystitis: safe steps in laparoscopic cholecystectomy for acute cholecystitis (with videos). J Hepatobiliary Pancreat Sci 25:73–86
Hugh TB, Kelly MD, Mekisic A (1997) Rouvière’s sulcus: a useful landmark in laparoscopic cholecystectomy. Br J Surg 84:1253–1254
Way LW, Stewart L, Gantert W, Liu K, Lee CM, Whang K, Hunter JG (2003) Causes and prevention of laparoscopic bile duct injuries: analysis of 252 cases from a human factors and cognitive psychology perspective. Ann Surg 237:460–469
Strasberg SM (2002) Avoidance of biliary injury during laparoscopic cholecystectomy. J Hepatobiliary Pancreat Surg 9:543–547
Mori T, Endo H, Misawa T, Yamaguchi S, Sakamoto Y, Inomata M, Sakai Y, Kakeji Y, Miyata H, Kitagawa Y, Watanabe M (2022) Involvement of a skill-qualified surgeon favorably influences outcomes of laparoscopic cholecystectomy performed for acute cholecystitis. Surg Endosc 36:5956–5963
Nakanuma H, Endo Y, Fujinaga A, Kawamura M, Kawasaki T, Masuda T, Hirashita T, Etoh T, Shinozuka K, Matsunobu Y, Kamiyama T, Ishikake M, Ebe K, Tokuyasu T, Inomata M (2023) An intraoperative artificial intelligence system identifying anatomical landmarks for laparoscopic cholecystectomy: a prospective clinical feasibility trial (J-SUMMIT-C-01). Surg Endosc 37:1933–1942
Tokuyasu T, Iwashita Y, Matsunobu Y, Kamiyama T, Ishikake M, Sakaguchi S, Ebe K, Tada K, Endo Y, Etoh T, Nakashima M, Inomata M (2021) Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy. Surg Endosc 35:1651–1658
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. arXiv:1804.02767
Shinozuka K, Turuda S, Fujinaga A, Nakanuma H, Kawamura M, Matsunobu Y, Tanaka Y, Kamiyama T, Ebe K, Endo Y, Etoh T, Inomata M, Tokuyasu T (2022) Artificial intelligence software available for medical devices: surgical phase recognition in laparoscopic cholecystectomy. Surg Endosc 36:7444–7452
Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning 97:6105–6114
Shamir RR, Duchin Y, Kim J, Sapiro G, Harel N (2018) Continuous Dice coefficient: a method for evaluating probabilistic segmentations. bioRxiv 1906:11031
Garrow CR, Kowalewski KF, Li L, Wagner M, Schmidt MW, Engelhardt S, Hashimoto DA, Kenngott HG, Bodenstedt S, Speidel S, Müller-Stich BP, Nickel F (2021) Machine learning for surgical phase recognition: a systematic review. Ann Surg 273:684–693
Madani A, Namazi B, Altieri MS, Hashimoto DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G, Brunt LM, Okrainec A, Alseidi A (2022) Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg 276:363–369
Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, Pessaux P, Mutter D, Marescaux J, Costamagna G, Dallemagne B, Padoy N (2022) Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 275:955–961
Acknowledgements
We would like to thank Dr. Toshio Bando of Oita Prefectural Hospital, Dr. Tadashi Ogawa of Cosmos Hospital, and Dr. Kazuhiro Yada of the National Hospital Organization Oita Medical Center, who evaluated this study as an external evaluation committee member.
Funding
The funding for this study was provided by the Japan Agency for Medical Research and Development (Grant No: JP19he2302003).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Disclosures
Atsuro Fujinaga, Yuichi Endo, Tsuyoshi Etoh, Masahiro Kawamura, Hiroaki Nakanuma, Takahide Kawasaki, Takashi Masuda, Teijiro Hirashita, Misako Kimura, Yusuke Matsunobu, Ken’ichi Shinozuka, Yuki Tanaka, Toshiya Kamiyama, Takemasa Sugita, Kenichi Morishima, Kohei Ebe, Tatsushi Tokuyasu and, Masafumi Inomata have no conflict of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary file1 (MP4 322184 KB)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fujinaga, A., Endo, Y., Etoh, T. et al. Development of a cross-artificial intelligence system for identifying intraoperative anatomical landmarks and surgical phases during laparoscopic cholecystectomy. Surg Endosc 37, 6118–6128 (2023). https://doi.org/10.1007/s00464-023-10097-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00464-023-10097-8