AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
ConditionLaparoscopic Cholecystectomy
Trial statusRecruiting
Trial phasePhase 3
Trial typeInterventional
Biological sexAll
Age18+
SponsorUniversity Health Network, Toronto
About this 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
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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
Design model
Parallel
Treatments tested in this trial
Artificial Intelligence Guidance Models
DeviceThe 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.
Treatment groups
70 Participants
are divided into 2 treatment groupsGroup A: Standard Surgical ProcedureNo intervention 0 interventions
Group B: Artificial Intelligence FeedbackExperimental treatment 1 intervention
Trial outcomes
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.
Time frame
Post-procedure through study completion (up to 1 year)Secondary outcomes
1
Dissections above Line of Safety
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.
Time frame
Post-procedure through study completion (up to 1 year)2
Surgeon-reported outcomes
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.
Time frame
Immediately after the procedure3
Observer-reported outcomes
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.
Time frame
During the procedure4
Post-operative chart review
Chart view after procedure to assess any complications or adverse events.
Time frame
Up to 30 days post-procedure.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov