AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
Evaluating the Clinical Impact of Artificial Intelligence on Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
University Health Network, Toronto
70 participants
Sep 17, 2025
INTERVENTIONAL
Conditions
Summary
Today, the majority of gallbladder removals surgeries are done using minimally invasive techniques through small cuts to help patients recover faster. However, these procedures are technically more challenging because surgeons have a restricted view of the patient's anatomy, which can increase the risk of serious complications. Artificial intelligence (AI) tools have been developed to guide surgeons during surgery and help them make safer decisions that reduce the risk of injury to the patient. This study will use a randomized controlled trial to compare outcomes between surgeries with AI assistance and standard procedures without AI. Primary Objective: To determine whether the AI improves surgeons' ability to achieve the Critical View of Safety, a key step for safe gallbladder removal, compared to standard procedures. Secondary Objectives: * Determine whether the AI helps the surgeon perform more safe dissections compared to the standard procedures. * Collect surgeon feedback on the use of AI during the procedure
Eligibility
Inclusion Criteria2
- Surgeon participants: Attending surgeons or fellows that perform laparoscopic cholecystectomy at University Health Network.
- Patients participants: Adults 18 years of age and over, scheduled for laparoscopic cholecystectomy surgery.
Exclusion Criteria2
- Surgeon participants: Anyone who is not a surgeon or fellow at University Health Network or that does not perform laparoscopic cholecystectomies.
- Patient participants: Any patient who is not having a laparoscopic cholecystectomy surgery.
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Interventions
The intervention will involve the use of two artificial intelligence (AI) models to provide surgical guidance during laparoscopic cholecystectomy procedures. The AI models will provide real-time feedback based on the live surgical feed (internal patient anatomy captured by laparoscopic camera) displayed on an operating room monitor. The GoNoGoNet model identifies safe and unsafe zones of dissection. This is done by showcasing a green overlay over safe zones of dissection, and a red overlay over unsafe zones of dissection. The DeepCVS model provides text-based feedback based on its assessment of the following three criteria defining the Critical View of Safety: 1) complete clearance of the hepatocystic triangle from fat and fibrous tissue, 2) only two structures visible entering the gallbladder (cystic artery and duct) and 3) the lower third of the gallbladder must be dissected off the liver bed, exposing the cystic plate.
Locations(2)
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NCT07186803