Laparoscopic CholecystectomyPhase 3NCT07186803
AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
A Phase 3 study testing Artificial Intelligence Guidance Models in people with Laparoscopic Cholecystectomy, run by University Health Network, Toronto at 2 sites in Canada.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Laparoscopic Cholecystectomy
- What is being tested?
- Artificial Intelligence Guidance Models
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 64 participants
- Where
- 2 sitesCanada
- Study dates
- 17 Sep 2025to 31 Aug 2026
- Phase
- Phase 3
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
2 sites in 1 country.
Canada2 sites
Toronto General Hospital
Toronto, Ontario, M5G 2C4
Toronto Western Hospital
Toronto, Ontario, M5T 2S8
About this study
Official title: Evaluating the Clinical Impact of Artificial Intelligence on 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.
Read more
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
Read the 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.
- Conditions
- Laparoscopic Cholecystectomy
- Being tested
- Artificial Intelligence Guidance Models
- Keywords
- artificial intelligence, laparoscopic cholecystectomy, safety, critical view of safety, line of safety
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence Guidance ModelsDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Standard Surgical Procedure | No intervention | Surgeons/fellows will perform the procedure, as per standard care measures. |
| Artificial Intelligence Feedback | Experimental | 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. Receives: Device: Artificial Intelligence Guidance Models |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Post-procedure through study completion (up to 1 year)
Secondary outcomes (4)
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.
Measured: Post-procedure through study completion (up to 1 year)
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.
Measured: Immediately after the procedure
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.
Measured: During the procedure
Post-operative chart review
Chart view after procedure to assess any complications or adverse events.
Measured: Up to 30 days post-procedure.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Sep 2025
- Study start17 Sep 2025
- Primary completion11 May 2026
- Study completion31 Aug 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Laparoscopic CholecystectomyStudies of the same condition, open and under way.
- More trials run by University Health Network, TorontoThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 3
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Parallel Cluster Design with Stratified Randomization. Each cluster will consist of one surgeon attending or fellow. Clusters are stratified based on professional characteristics (Eg. experience level) before randomization to the intervention or control group.
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 64 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Health Network, Toronto
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Amin Madani, University Health Network, Toronto
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07186803
- Sponsor's protocol ID
- 25-5053
- Status last verified
- Sep 2026
- First submitted
- 13 Sep 2025
- Last updated
- 15 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Clinical Impact of Artificial Intelligence on Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07186803
- Acronym / sponsor ID
- 25-5053
- Conditions
- Laparoscopic Cholecystectomy
- Keywords
- artificial intelligence, laparoscopic cholecystectomy, safety, critical view of safety, line of safety
- Phase
- Phase 3
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 64 participants (actual)
- Lead sponsor
- University Health Network, TorontoUniversity, hospital or other
- Study start
- 17 Sep 2025 (actual)
- Primary completion
- 11 May 2026 (actual)
- Study completion
- 31 Aug 2026 (actual)
- First posted
- 22 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
- Model description
- Parallel Cluster Design with Stratified Randomization. Each cluster will consist of one surgeon attending or fellow. Clusters are stratified based on professional characteristics (Eg. experience level) before randomization to the intervention or control group.
| Arm | Type | Receives |
|---|---|---|
| Standard Surgical Procedure | No intervention | –Surgeons/fellows will perform the procedure, as per standard care measures. |
| Artificial Intelligence Feedback | Experimental | Device: Artificial Intelligence Guidance ModelsSurgeons 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence Guidance ModelsThe 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. | Device | Artificial Intelligence Feedback |
Primary outcome
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.
4 secondary outcomes
Dissections above Line of Safety
Time frame: Post-procedure through study completion (up to 1 year)
Surgeon-reported outcomes
Time frame: Immediately after the procedure
Observer-reported outcomes
Time frame: During the procedure
Post-operative chart review
Time frame: Up to 30 days post-procedure.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Sep 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A Phase 3 study testing Artificial Intelligence Guidance Models in people with Laparoscopic Cholecystectomy, run by University Health Network, Toronto at 2 sites in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 3 studies compare the treatment with the current standard treatment or a placebo in a large group, to confirm how well it works and watch for side effects.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Critical View of Safety Achievement Rate (time frame: Post-procedure through study completion (up to 1 year)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Sep 2026.