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11-09-2023 | Artificial Intelligence | 2023 SAGES Oral

Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy

Authors: Muhammad Uzair Khalid, Simon Laplante, Caterina Masino, Adnan Alseidi, Shiva Jayaraman, Haochi Zhang, Pouria Mashouri, Sergey Protserov, Jaryd Hunter, Michael Brudno, Amin Madani

Published in: Surgical Endoscopy | Issue 12/2023

Abstract

Introduction

Bile duct injuries (BDIs) are a significant source of morbidity among patients undergoing laparoscopic cholecystectomy (LC). GoNoGoNet is an artificial intelligence (AI) algorithm that has been developed and validated to identify safe (“Go”) and dangerous (“No-Go”) zones of dissection during LC, with the potential to prevent BDIs through real-time intraoperative decision-support. This study evaluates GoNoGoNet's ability to predict Go/No-Go zones during LCs with BDIs.

Methods and procedures

Eleven LC videos with BDI (BDI group) were annotated by GoNoGoNet. All tool-tissue interactions, including the one that caused the BDI, were characterized in relation to the algorithm’s predicted location of Go/No-Go zones. These were compared to another 11 LC videos with cholecystitis (control group) deemed to represent “safe cholecystectomy” by experts. The probability threshold of GoNoGoNet annotations were then modulated to determine its relationship to Go/No-Go predictions. Data is shown as % difference [99% confidence interval].

Results

Compared to control, the BDI group showed significantly greater proportion of sharp dissection (+ 23.5% [20.0–27.0]), blunt dissection (+ 32.1% [27.2–37.0]), and total interactions (+ 33.6% [31.0–36.2]) outside of the Go zone. Among injury-causing interactions, 4 (36%) were in the No-Go zone, 2 (18%) were in the Go zone, and 5 (45%) were outside both zones, after maximizing the probability threshold of the Go algorithm.

Conclusion

AI has potential to detect unsafe dissection and prevent BDIs through real-time intraoperative decision-support. More work is needed to determine how to optimize integration of this technology into the operating room workflow and adoption by end-users.

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Title: Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy
Authors: Muhammad Uzair Khalid
Simon Laplante
Caterina Masino
Adnan Alseidi
Shiva Jayaraman
Haochi Zhang
Pouria Mashouri
Sergey Protserov
Jaryd Hunter
Michael Brudno
Amin Madani
Publication date: 11-09-2023
Publisher: Springer US
Keywords: Artificial Intelligence
Cholecystectomy
Published in: Surgical Endoscopy / Issue 12/2023
Print ISSN: 0930-2794
Electronic ISSN: 1432-2218
DOI: https://doi.org/10.1007/s00464-023-10403-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy

Abstract

Introduction

Methods and procedures

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Introduction

Methods and procedures

Results

Conclusion

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