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Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy

  • 2022 SAGES Oral
  • Published:
Surgical Endoscopy Aims and scope Submit manuscript

Abstract

Background

Many surgical adverse events, such as bile duct injuries during laparoscopic cholecystectomy (LC), occur due to errors in visual perception and judgment. Artificial intelligence (AI) can potentially improve the quality and safety of surgery, such as through real-time intraoperative decision support. GoNoGoNet is a novel AI model capable of identifying safe (“Go”) and dangerous (“No-Go”) zones of dissection on surgical videos of LC. Yet, it is unknown how GoNoGoNet performs in comparison to expert surgeons. This study aims to evaluate the GoNoGoNet’s ability to identify Go and No-Go zones compared to an external panel of expert surgeons.

Methods

A panel of high-volume surgeons from the SAGES Safe Cholecystectomy Task Force was recruited to draw free-hand annotations on frames of prospectively collected videos of LC to identify the Go and No-Go zones. Expert consensus on the location of Go and No-Go zones was established using Visual Concordance Test pixel agreement. Identification of Go and No-Go zones by GoNoGoNet was compared to expert-derived consensus using mean F1 Dice Score, and pixel accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

A total of 47 frames from 25 LC videos, procured from 3 countries and 9 surgeons, were annotated simultaneously by an expert panel of 6 surgeons and GoNoGoNet. Mean (± standard deviation) F1 Dice score were 0.58 (0.22) and 0.80 (0.12) for Go and No-Go zones, respectively. Mean (± standard deviation) accuracy, sensitivity, specificity, PPV and NPV for the Go zones were 0.92 (0.05), 0.52 (0.24), 0.97 (0.03), 0.70 (0.21), and 0.94 (0.04) respectively. For No-Go zones, these metrics were 0.92 (0.05), 0.80 (0.17), 0.95 (0.04), 0.84 (0.13) and 0.95 (0.05), respectively.

Conclusions

AI can be used to identify safe and dangerous zones of dissection within the surgical field, with high specificity/PPV for Go zones and high sensitivity/NPV for No-Go zones. Overall, model prediction was better for No-Go zones compared to Go zones. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.

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Acknowledgements

We acknowledge the work of Mr. Robert Messina for developing the Think Like A Surgeon annotation tool.

Funding

SAGES Research Grant Award.

Author information

Authors and Affiliations

  1. Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada

    Simon Laplante, Parmiss Kiani, Allan Okrainec & Amin Madani

  2. Department of Surgery, University of Toronto, Toronto, ON, Canada

    Simon Laplante, Allan Okrainec & Amin Madani

  3. Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Babak Namazi

  4. Department of Surgery, University Hospitals, Cleveland, OH, USA

    Daniel A. Hashimoto

  5. Department of Surgery, University of California, San Francisco, CA, USA

    Adnan Alseidi

  6. Instituto de Gastroenterologia Boliviano Japones, Cochabamba, Bolivia

    Mauricio Pasten

  7. Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA

    L. Michael Brunt

  8. Department of Surgery, Northeast Georgia Medical Center, Georgia, USA

    Sujata Gill

  9. Department of Surgery, Texas Tech Paul L Foster School of Medicine, El Paso, TX, USA

    Brian Davis

  10. Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Matthew Bloom

  11. Department of Surgery, Boston medical center, Boston, MA, USA

    Luise Pernar

  12. MIS Fellow, Toronto Western Hospital, Division of General Surgery, 8MP-325., 399 Bathurst St, Toronto,, ON, M5T 2S8, Canada

    Simon Laplante

Authors
  1. Simon Laplante
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Corresponding author

Correspondence to Simon Laplante.

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Disclosures

Drs. Amin Madani is a consultant for Activ Surgical. Dr. Daniel Hashimoto is a consultant for Activ Surgical, Johnson & Johnson Institute, Verily Life Sciences, Worrell, and Mosaic Research Management. Dr. Allan Okrainec has received honoraria for speaking and teaching from Medtronic, Ethicon, and Merck. Parmiss Kiani has no disclosures. Drs. Simon Laplante, Babak Namazi, Brian Davis, Mauricio Pasten, L. Michael Brunt, Adnan Alseidi, Luise Pernar, Matthew Bloom and Sujata Gill, have no conflicts of interest or financial ties to disclose.

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Laplante, S., Namazi, B., Kiani, P. et al. Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy. Surg Endosc 37, 2260–2268 (2023). https://doi.org/10.1007/s00464-022-09439-9

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  • DOI: https://doi.org/10.1007/s00464-022-09439-9

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