Recruiting
Phase 3

AI vs. Standard

Sponsor:

University Health Network, Toronto

Code:

NCT07186803

Conditions

Laparoscopic Cholecystectomy

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Interventions

Artificial Intelligence Guidance Models

Study Details

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

Study ID

NCT07186803

Start date

Sep 17, 2025

Status verified date

Jan, 2026

Completion date

Jul 30, 2026

Anticipated

Primary completion date

Jun 30, 2026

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Inclusion Criteria:

  • 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 Criteria:

  • 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.

Study Design

Enrollment

70 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Prevention

Interventions and Outcome Measures

Arms

no intervention: Standard Surgical Procedure

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

experimental: Artificial Intelligence Feedback

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.

Interventions

Artificial Intelligence Guidance Models

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.

Primary outcome measure

  • Critical View of Safety Achievement Rate [ Time Frame: Post-procedure through study completion (up to 1 year) ]

Central Contacts and Locations

Central contacts

Ariana Walji, BSc, MSc Candidate

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

Locations

Toronto General Hospital

Recruiting

Toronto, Ontario, Canada, M5G 2C4

Contacts

Ariana Walji, BSc, MSc Candidate

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

Principal Investigator:

Amin Madani, MD, PhD

Toronto Western Hospital

Recruiting

Toronto, Ontario, Canada, M5T 2S8

Contacts

Ariana Walji, BSc, MSc Candidate

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

Principal Investigator:

Amin Madani, MD, PhD

More Information

Sponsor

University Health Network, Toronto

Last update posted

Jan 13, 2026

Last verified

Jan, 2026

Keywords

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

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by University Health Network, Toronto on 2026-01-13.