AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial

University Health Network, Toronto

Status and phase: Enrolling; Phase 3

Conditions: Laparoscopic Cholecystectomy

Treatments: Device: Artificial Intelligence Guidance Models

Study type: Interventional

Funder types: Other

Identifiers

NCT07186803

25-5053

Details and patient eligibility

About

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

Full description

To measure the clinical impact of artificial intelligence (AI) guidance on the achievement of safety milestones in laparoscopic cholecystectomy compared to standard care, the study team will conduct a randomized controlled trial of 10 surgeons or fellows and 50 patients undergoing laparoscopic cholecystectomy procedures at two hospital sites part of the University Health Network in Toronto, Ontario, Canada (Toronto General Hospital and Toronto Western Hospital). Surgeons or fellows randomized to the intervention group (AI) will each perform 5 procedures using two AI models that provide real-time feedback to guide safe dissections and the achievement of the critical view of safety. Surgeons or fellows randomized to the control group will each perform 5 procedures using the standard care approach. Internal laparoscopic recordings will be collected from both the intervention and control groups for post-operative outcome analysis by blinded expert surgeon reviewers.

The research team will evaluate whether the use of AI during the procedure improves the achievement rate of the Critical View of Safety as compared to standard procedures.

Additionally, secondary outcomes will be assessed including the proportion of dissections that occurred above the line of safety, surgeon feedback on the use of AI during the procedure, observational notes recorded by the research coordinator present during each procedure, and 30-day post operation chart review.

Enrollment: 70 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Prevention

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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70 participants in 2 patient groups

Standard Surgical Procedure

No Intervention group

Description:

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

Artificial Intelligence Feedback

Experimental group

Description:

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.

Treatment:

Device: Artificial Intelligence Guidance Models