Ignite Creation Date:
2025-12-24 @ 7:35 PM
Ignite Modification Date:
2025-12-29 @ 2:35 AM
Study NCT ID:
NCT07186803
Status:
RECRUITING
Last Update Posted:
2025-09-22
First Post:
2025-09-13
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['PHASE3'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL', 'interventionModelDescription': '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.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 70}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-09', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2026-07-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-18', 'studyFirstSubmitDate': '2025-09-13', 'studyFirstSubmitQcDate': '2025-09-18', 'lastUpdatePostDateStruct': {'date': '2025-09-22', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-09-22', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Critical View of Safety Achievement Rate', 'timeFrame': 'Post-procedure through study completion (up to 1 year)', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Dissections above Line of Safety', 'timeFrame': 'Post-procedure through study completion (up to 1 year)', 'description': '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.'}, {'measure': 'Surgeon-reported outcomes', 'timeFrame': 'Immediately after the procedure', 'description': '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.'}, {'measure': 'Observer-reported outcomes', 'timeFrame': 'During the procedure', 'description': '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.'}, {'measure': 'Post-operative chart review', 'timeFrame': 'Up to 30 days post-procedure.', 'description': 'Chart view after procedure to assess any complications or adverse events.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence', 'laparoscopic cholecystectomy', 'safety', 'critical view of safety', 'line of safety'], 'conditions': ['Laparoscopic Cholecystectomy']}, 'descriptionModule': {'briefSummary': "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.\n\nPrimary 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.\n\nSecondary Objectives:\n\n* Determine whether the AI helps the surgeon perform more safe dissections compared to the standard procedures.\n* Collect surgeon feedback on the use of AI during the procedure", 'detailedDescription': '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.\n\nThe 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.\n\nAdditionally, 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Surgeon participants: Attending surgeons or fellows that perform laparoscopic cholecystectomy at University Health Network.\n* Patients participants: Adults 18 years of age and over, scheduled for laparoscopic cholecystectomy surgery.\n\nExclusion Criteria:\n\n* Surgeon participants: Anyone who is not a surgeon or fellow at University Health Network or that does not perform laparoscopic cholecystectomies.\n* Patient participants: Any patient who is not having a laparoscopic cholecystectomy surgery.'}, 'identificationModule': {'nctId': 'NCT07186803', 'briefTitle': 'AI and Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial', 'organization': {'class': 'OTHER', 'fullName': 'University Health Network, Toronto'}, 'officialTitle': 'Evaluating the Clinical Impact of Artificial Intelligence on Safety in Laparoscopic Cholecystectomy: A Randomized Controlled Trial', 'orgStudyIdInfo': {'id': '25-5053'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Standard Surgical Procedure', 'description': 'Surgeons/fellows will perform the procedure, as per standard care measures.'}, {'type': 'EXPERIMENTAL', 'label': 'Artificial Intelligence Feedback', '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.', 'interventionNames': ['Device: Artificial Intelligence Guidance Models']}], 'interventions': [{'name': 'Artificial Intelligence Guidance Models', 'type': 'DEVICE', 'description': '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.', 'armGroupLabels': ['Artificial Intelligence Feedback']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'M5G 2C4', 'city': 'Toronto', 'state': 'Ontario', 'status': 'RECRUITING', 'country': 'Canada', 'contacts': [{'name': 'Ariana Walji, BSc, MSc Candidate', 'role': 'CONTACT', 'email': 'ariana.walji@uhn.ca', 'phone': '416-603-5185', 'phoneExt': '2294'}, {'name': 'Amin Madani, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Toronto General Hospital', 'geoPoint': {'lat': 43.70643, 'lon': -79.39864}}, {'zip': 'M5T 2S8', 'city': 'Toronto', 'state': 'Ontario', 'status': 'RECRUITING', 'country': 'Canada', 'contacts': [{'name': 'Ariana Walji, BSc, MSc Candidate', 'role': 'CONTACT', 'email': 'ariana.walji@uhn.ca', 'phone': '416-603-5185', 'phoneExt': '2294'}, {'name': 'Amin Madani, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Toronto Western Hospital', 'geoPoint': {'lat': 43.70643, 'lon': -79.39864}}], 'centralContacts': [{'name': 'Ariana Walji, BSc, MSc Candidate', 'role': 'CONTACT', 'email': 'ariana.walji@uhn.ca', 'phone': '416-603-5185', 'phoneExt': '2294'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Health Network, Toronto', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Endocrine and Acute Care Surgeon and Researcher at The Institute for Education Research', 'investigatorFullName': 'Amin Madani', 'investigatorAffiliation': 'University Health Network, Toronto'}}}}