Skip to main content

Advertisement

SpringerLink
Log in

Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy

  • 2023 SAGES Oral
  • Published:
Surgical Endoscopy Aims and scope Submit manuscript

Abstract

Background

The Critical View of Safety (CVS) was proposed in 1995 to prevent bile duct injury during laparoscopic cholecystectomy (LC). The achievement of CVS was evaluated subjectively. This study aimed to develop an artificial intelligence (AI) system to evaluate CVS scores in LC.

Materials and methods

AI software was developed to evaluate the achievement of CVS using an algorithm for image classification based on a deep convolutional neural network. Short clips of hepatocystic triangle dissection were converted from 72 LC videos, and 23,793 images were labeled for training data. The learning models were examined using metrics commonly used in machine learning.

Results

The mean values of precision, recall, F-measure, specificity, and overall accuracy for all the criteria of the best model were 0.971, 0.737, 0.832, 0.966, and 0.834, respectively. It took approximately 6 fps to obtain scores for a single image.

Conclusions

Using the AI system, we successfully evaluated the achievement of the CVS criteria using still images and videos of hepatocystic triangle dissection in LC. This encourages surgeons to be aware of CVS and is expected to improve surgical safety.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Reynolds W Jr. (2001) The first laparoscopic cholecystectomy. JSLS 5:89–94

    PubMed  PubMed Central  Google Scholar 

  2. Melton GB, Lillemoe KD, Cameron JL, Sauter PA, Coleman J, Yeo CJ (2002) Major bile duct injuries associated with laparoscopic cholecystectomy: effect of surgical repair on quality of life. Ann Surg 235:888–895

    Article  PubMed  PubMed Central  Google Scholar 

  3. Roslyn JJ, Binns GS, Hughes EF, Saunders-Kirkwood K, Zinner MJ, Cates JA (1993) Open cholecystectomy. A contemporary analysis of 42,474 patients. Ann Surg 218:129–137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Strasberg SM, Hertl M, Soper NJ (1995) An analysis of the problem of biliary injury during laparoscopic cholecystectomy. J Am Coll Surg 180:101–125

    CAS  PubMed  Google Scholar 

  5. 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

    Article  PubMed  Google Scholar 

  6. Strasberg SM (2019) A three-step conceptual roadmap for avoiding bile duct injury in laparoscopic cholecystectomy: an invited perspective review. J Hepatobiliary Pancreat Sci 26:123–127

    Article  PubMed  Google Scholar 

  7. Strasberg SM, Brunt LM (2017) The critical view of safety: why it is not the only method of ductal identification within the standard of care in laparoscopic cholecystectomy. Ann Surg 265:464–465

    Article  PubMed  Google Scholar 

  8. Hugh TB (2002) New strategies to prevent laparoscopic bile duct injury—surgeons can learn from pilots. Surgery 132:826–835

    Article  PubMed  Google Scholar 

  9. 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

    Article  PubMed  Google Scholar 

  10. Conrad C, Wakabayashi G, Asbun HJ, Dallemagne B, Demartines N, Diana M, Fuks D, Giménez ME, Goumard C, Kaneko H, Memeo R, Resende A, Scatton O, Schneck AS, Soubrane O, Tanabe M, van den Bos J, Weiss H, Yamamoto M, Marescaux J, Pessaux P (2017) IRCAD recommendation on safe laparoscopic cholecystectomy. J Hepatobiliary Pancreat Sci 24:603–615

    Article  PubMed  Google Scholar 

  11. de’Angelis N, Catena F, Memeo R, Coccolini F, Martínez-Pérez A, Romeo OM, De Simone B, Di Saverio S, Brustia R, Rhaiem R, Piardi T, Conticchio M, Marchegiani F, Beghdadi N, Abu-Zidan FM, Alikhanov R, Allard MA, Allievi N, Amaddeo G, Ansaloni L, Andersson R, Andolfi E, Azfar M, Bala M, Benkabbou A, Ben-Ishay O, Bianchi G, Biffl WL, Brunetti F, Carra MC, Casanova D, Celentano V, Ceresoli M, Chiara O, Cimbanassi S, Bini R, Coimbra R, Luigi de’Angelis G, Decembrino F, De Palma A, de Reuver PR, Domingo C, Cotsoglou C, Ferrero A, Fraga GP, Gaiani F, Gheza F, Gurrado A, Harrison E, Henriquez A, Hofmeyr S, Iadarola R, Kashuk JL, Kianmanesh R, Kirkpatrick AW, Kluger Y, Landi F, Langella S, Lapointe R, Le Roy B, Luciani A, Machado F, Maggi U, Maier RV, Mefire AC, Hiramatsu K, Ordoñez C, Patrizi F, Planells M, Peitzman AB, Pekolj J, Perdigao F, Pereira BM, Pessaux P, Pisano M, Puyana JC, Rizoli S, Portigliotti L, Romito R, Sakakushev B, Sanei B, Scatton O, Serradilla-Martin M, Schneck AS, Sissoko ML, Sobhani I, Ten Broek RP, Testini M, Valinas R, Veloudis G, Vitali GC, Weber D, Zorcolo L, Giuliante F, Gavriilidis P, Fuks D, Sommacale D (2021) 2020 WSES guidelines for the detection and management of bile duct injury during cholecystectomy. World J Emerg Surg 16:30

    Article  PubMed  PubMed Central  Google Scholar 

  12. 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

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nijssen MA, Schreinemakers JM, Meyer Z, van der Schelling GP, Crolla RM, Rijken AM (2015) Complications after laparoscopic cholecystectomy: a video evaluation study of whether the critical view of safety was reached. World J Surg 39:1798–1803

    Article  CAS  PubMed  Google Scholar 

  14. 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

    Article  PubMed  Google Scholar 

  15. 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 (2022) 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

    Article  PubMed  Google Scholar 

  16. 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

    Article  PubMed  PubMed Central  Google Scholar 

  17. Asai K, Iwashita Y, Ohyama T, Endo I, Hibi T, Umezawa A, Suzuki K, Watanabe M, Kurata M, Mori Y, Higashida M, Kumamoto Y, Shindoh J, Yoshida M, Honda G, Misawa T, Abe Y, Nagakawa Y, Toyota N, Yamada S, Norimizu S, Matsumura N, Sata N, Sunagawa H, Ito M, Takeda Y, Nakamura Y, Rikiyama T, Higuchi R, Gocho T, Homma Y, Hirashita T, Kanemoto H, Nozawa M, Watanabe Y, Kohga A, Yazawa T, Tajima H, Nakahira S, Asaoka T, Yoshioka R, Fukuzawa J, Fujioka S, Hata T, Haruta H, Asano Y, Nomura R, Matsumoto J, Kameyama N, Miyoshi A, Urakami H, Seyama Y, Morikawa T, Kawano Y, Ikoma H, Kin DHK, Takada T, Yamamoto M (2022) Application of a novel surgical difficulty grading system during laparoscopic cholecystectomy. J Hepatobiliary Pancreat Sci 29:758–767

    Article  PubMed  Google Scholar 

  18. Sanford DE, Strasberg SM (2014) A simple effective method for generation of a permanent record of the Critical View of Safety during laparoscopic cholecystectomy by intraoperative “doublet” photography. J Am Coll Surg 218:170–178

    Article  PubMed  Google Scholar 

  19. Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. ICML. https://doi.org/10.48550/arXiv.1905.11946

    Article  Google Scholar 

  20. Foret P, Kleiner A, Mobahi H, Neyshabur B (2020) Sharpness-aware minimization for efficiently improving generalization. ICML. https://doi.org/10.48550/arXiv.2010.01412

    Article  Google Scholar 

  21. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2016) Grad-CAM: visual explanations from deep networks via gradient-based localization. ICML. https://doi.org/10.48550/arXiv.1610.02391

    Article  Google Scholar 

  22. Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, Pessaux P, Mutter D, Marescaux J, Costamagna G, Dallemagne B, Padoy N (2020) Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 275:955–961

    Article  PubMed  Google Scholar 

  23. Kitaguchi D, Takeshita N, Matsuzaki H, Takano H, Owada Y, Enomoto T, Oda T, Miura H, Yamanashi T, Watanabe M, Sato D, Sugomori Y, Hara S, Ito M (2020) Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach. Surg Endosc 34:4924–4931

    Article  PubMed  Google Scholar 

  24. Stefanidis D, Chintalapudi N, Anderson-Montoya B, Oommen B, Tobben D, Pimentel M (2017) How often do surgeons obtain the critical view of safety during laparoscopic cholecystectomy? Surg Endosc 31:142–146

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Tomoko Kanda and Chiho Tomimatsu for their office work concerning this study. We thank Yohei Soeda for assisting with the study. We thank Editage (www.editage.com) for English language editing.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Gastroenterological and Pediatric Surgery, Faculty of Medicine, Oita University, Oita, Japan

    Masahiro Kawamura, Yuichi Endo, Atsuro Fujinaga, Hiroki Orimoto, Shota Amano, Takahide Kawasaki, Yoko Kawano, Takashi Masuda, Teijiro Hirashita & Masafumi Inomata

  2. Department of Information System and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Misako Kimura, Aika Ejima, Yusuke Matsunobu, Ken’ichi Shinozuka & Tatsushi Tokuyasu

Authors
  1. Masahiro Kawamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuichi Endo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atsuro Fujinaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroki Orimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shota Amano
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Takahide Kawasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yoko Kawano
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Takashi Masuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Teijiro Hirashita
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Misako Kimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Aika Ejima
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yusuke Matsunobu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ken’ichi Shinozuka
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tatsushi Tokuyasu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Masafumi Inomata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masahiro Kawamura.

Ethics declarations

Disclosures

Masahiro Kawamura, Yuichi Endo, Atsuro Fujinaga, Hiroki Orimoto, Shota Amano, Takahide Kawasaki, Yoko Kawano, Takashi Masuda, Teijiro Hirashita, Misako Kimura, Aika Ejima, Yusuke Matsunobu, Ken’ichi Shinozuka, Tatsushi Tokuyasu, and Masafumi Inomata have no conflicts of interest or financial ties to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawamura, M., Endo, Y., Fujinaga, A. et al. Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy. Surg Endosc 37, 8755–8763 (2023). https://doi.org/10.1007/s00464-023-10328-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00464-023-10328-y

Keywords

Search

Navigation