MedPath

AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial

Not Applicable
Recruiting
Conditions
Laparoscopic Cholecystectomy
Registration Number
NCT07186803
Lead Sponsor
University Health Network, Toronto
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

Detailed 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.

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
70
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

Study Type
INTERVENTIONAL
Study Design
PARALLEL
Primary Outcome Measures
NameTimeMethod
Critical View of Safety Achievement RatePost-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 Outcome Measures
NameTimeMethod
Dissections above Line of SafetyPost-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.

Surgeon-reported outcomesImmediately 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.

Observer-reported outcomesDuring 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.

Post-operative chart reviewUp to 30 days post-procedure.

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

Trial Locations

Locations (2)

Toronto General Hospital

🇨🇦

Toronto, Ontario, Canada

Toronto Western Hospital

🇨🇦

Toronto, Ontario, Canada

Toronto General Hospital
🇨🇦Toronto, Ontario, Canada
Ariana Walji, BSc, MSc Candidate
Contact
416-603-5185
ariana.walji@uhn.ca
Amin Madani, MD, PhD
Principal Investigator

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