AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial

NCT07186803 | Recruiting Phase 3

• 1 other identifier: 25-5053
• Study Type: interventional
• Target: 70
• Locations: 1 country
• Sites: 2

Brief 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

Conditions

Laparoscopic Cholecystectomy

Keywords

artificial intelligence; laparoscopic cholecystectomy; safety; critical view of safety; line of safety

Outcome Measures

Primary Outcomes (1)

• Critical View of Safety Achievement Rate

  Blinded expert surgeons will review the laparoscopic video recordings to determine whether the Critical View of Safety (CVS) was fully achieved, defined as meeting all three required criteria. The proportion of cases with fully achieved CVS in the intervention group will be compared with the proportion in the control group.

  Post-procedure through study completion (up to 1 year)

Secondary Outcomes (4)

• Dissections above Line of Safety

  Post-procedure through study completion (up to 1 year)

• Surgeon-reported outcomes

  Immediately after the procedure

• Observer-reported outcomes

  During the procedure

• Post-operative chart review

  Up to 30 days post-procedure.

Study Arms (2)

Standard Surgical Procedure

NO INTERVENTION

Surgeons/fellows will perform the procedure, as per standard care measures.

Artificial Intelligence Feedback

EXPERIMENTAL

Surgeons or fellows in the intervention group will have access to two AI models during their procedure. A research coordinator will operate and monitor the AI models, which are displayed on a single monitor in the operating room. Participants may request to toggle between models or turn them off at any point during the procedure, as per their needs.

Device: Artificial Intelligence Guidance Models

Interventions

Artificial Intelligence Guidance Models DEVICE

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.

Artificial Intelligence Feedback

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• University Health Network, Toronto: lead

Study Sites (2)

Toronto General Hospital

Toronto, Ontario, M5G 2C4, Canada

RECRUITING

Toronto Western Hospital

Toronto, Ontario, M5T 2S8, Canada

RECRUITING

Central Study Contacts

Ariana Walji, BSc, MSc Candidate

CONTACT

416-603-5185; ariana.walji@uhn.ca

Study Design

• Study Type: interventional
• Phase: phase 3
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Endocrine and Acute Care Surgeon and Researcher at The Institute for Education Research

Model Details: Parallel Cluster Design with Stratified Randomization. Each cluster will consist of one surgeon attending or fellow. Clusters are stratified based on professional characteristics (Eg. experience level) before randomization to the intervention or control group.

Study Record Dates

• First Submitted: September 13, 2025
• First Posted: September 22, 2025
• Study Start: September 17, 2025
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): July 30, 2026
• Last Updated: January 13, 2026

Record last verified: 2026-01

Locations

CA (2)