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NCT Number: NCT07186803

AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial

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

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Key information

Conditions

Laparoscopic Cholecystectomy

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Phase 3

Primary location

Toronto General Hospital, Toronto, Ontario, Canada

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

  1. Critical View of Safety Achievement Rate

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

    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.

Secondary outcomes

  1. Dissections above Line of Safety

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

    Blinded expert surgeons will review the laparoscopic video recordings to determine the proportion of dissections performed above the line of safety. The mean proportion across cases in the intervention group will be compared with the mean proportion across cases in the control group.

  2. Surgeon-reported outcomes

    Time frame: Immediately after the procedure

    Surgeon/fellows in the intervention group will provide feedback regarding the use of artificial intelligence during the procedure through a survey questionnaire provided post-surgery.

  3. Observer-reported outcomes

    Time frame: During the procedure

    The research coordinator will note down observations during all cases (eg. number of mentoring episodes). Audio recording will also be captured to verify written notes.

  4. Post-operative chart review

    Time frame: Up to 30 days post-procedure.

    Chart view after procedure to assess any complications or adverse events.

Study contacts

Contact information is provided by the study sponsor or research team.

Ariana Walji, BSc, MSc Candidate

CONTACT

[email protected]

416-603-5185 ext. 2294

Sponsors and collaborators

Lead sponsor

University Health Network, Toronto

Other

Registry information

Official study title

Evaluating the Clinical Impact of Artificial Intelligence on Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Sep 22, 2025
Registry last updated
Jan 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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