Simon Laplante1,2,3, Babak Namazi4, Parmiss Kiani5, Daniel A Hashimoto6, Adnan Alseidi7, Mauricio Pasten8, L Michael Brunt9, Sujata Gill10, Brian Davis11, Matthew Bloom12, Luise Pernar13, Allan Okrainec5,14, Amin Madani5,14. 1. Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada. simon.laplante@mail.utoronto.ca. 2. Department of Surgery, University of Toronto, Toronto, ON, Canada. simon.laplante@mail.utoronto.ca. 3. MIS Fellow, Toronto Western Hospital, Division of General Surgery, 8MP-325., 399 Bathurst St, Toronto,, ON, M5T 2S8, Canada. simon.laplante@mail.utoronto.ca. 4. Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA. 5. Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada. 6. Department of Surgery, University Hospitals, Cleveland, OH, USA. 7. Department of Surgery, University of California, San Francisco, CA, USA. 8. Instituto de Gastroenterologia Boliviano Japones, Cochabamba, Bolivia. 9. Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA. 10. Department of Surgery, Northeast Georgia Medical Center, Georgia, USA. 11. Department of Surgery, Texas Tech Paul L Foster School of Medicine, El Paso, TX, USA. 12. Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 13. Department of Surgery, Boston medical center, Boston, MA, USA. 14. Department of Surgery, University of Toronto, Toronto, ON, Canada.
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.
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.
Authors: Lawrence W Way; Lygia Stewart; Walter Gantert; Kingsway Liu; Crystine M Lee; Karen Whang; John G Hunter Journal: Ann Surg Date: 2003-04 Impact factor: 12.969
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Authors: Amin Madani; Babak Namazi; Maria S Altieri; Daniel A Hashimoto; Angela Maria Rivera; Philip H Pucher; Allison Navarrete-Welton; Ganesh Sankaranarayanan; L Michael Brunt; Allan Okrainec; Adnan Alseidi Journal: Ann Surg Date: 2020-11-13 Impact factor: 13.787