Cholecystitis Clinical Trial
Official title:
Feasibility and Utility of Artificial Intelligence (AI) / Machine Learning (ML) - Driven Advanced Intraoperative Visualization and Identification of Critical Anatomic Structures and Procedural Phases in Laparoscopic Cholecystectomy
NCT number | NCT05775133 |
Other study ID # | 20220620 |
Secondary ID | |
Status | Recruiting |
Phase | N/A |
First received | |
Last updated | |
Start date | August 1, 2023 |
Est. completion date | December 2025 |
The goal of this study is to evaluate the utility and efficacy of an artificial intelligence (AI) model at identifying structures and phases of surgery compared to traditional white light assessment by trained surgeons. Surgeons will perform the procedure in their standard practice, while the AI model analyzes data from the laparoscopic camera. Surgeons will be asked to audibly state when they identify structures and enter different phases of the surgical procedure. The AI will not alter the surgeon's view or be visible to the surgeon, and the surgeon will perform the procedure in the exact same fashion as they typically do.
Status | Recruiting |
Enrollment | 120 |
Est. completion date | December 2025 |
Est. primary completion date | December 2025 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria - All patients age > 18 years old who are planned for elective laparoscopic cholecystectomy; spoken command and literacy in the native language spoken at each participating center; ability to understand and follow study procedures; and having provided signed consent. - Eligible patients will be screened and assigned as per risk calculator for difficulty of LC - Diagnosis: - All patients with clinical suspicion and diagnosis of symptomatic cholelithiasis or cholecystitis planned for cholecystectomy. - Typical imaging as per standard workup findings including US, CT and/or MRI. Plain radiographs and contrast imaging may be obtained by referring physicians and are helpful for confirming the clinical diagnosis. - Prior therapy: o Patients with prior surgery are eligible for enrollment. - Laboratory: - Hemoglobin > 9 g/dL - Platelet count =75,000/µL (may receive transfusions) - Normal prothrombin time (PT), partial thromboplastin time (PTT) and international normalized ratio (INR) < 1.5 x upper limit of normal (including patients on prophylactic anticoagulation) - Liver Function Test - Renal function: Age-adjusted normal serum creatinine - Adequate pulmonary function: Defined as no dyspnea at rest, and a pulse oximetry >94% on room air if there is any clinical indication for determination. Exclusion Criteria - Non-elective acute cholecystectomy will be excluded. - Patients assigned to FDA cleared ICG-based visualization are contraindicated for any chronic renal dysfunction, potential drug interaction, history of allergy to ICG or anaphylaxis, and pregnancy. - Patients eligible for cholecystectomy, exclusion criteria include known allergy to ICG; coagulopathy or known, pre-existing liver disease; pregnancy or breast-feeding; or being of reproductive age with pregnancy possible and not ruled out. - Patients currently in any investigational agents. - Adults unable to consent - Individuals under 18 years of age - Pregnant women - Prisoners - Vulnerable populations |
Country | Name | City | State |
---|---|---|---|
United States | Memorial Hermann Texas Medical Center | Houston | Texas |
Lead Sponsor | Collaborator |
---|---|
Activ Surgical |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Precision and accuracy | Precision and accuracy of AI/ML model at identifying procedural phases and critical anatomic structures in laparoscopic cholecystectomy (LC).
Precision will be calculated as True Positives / (True Positives + False Positives) Accuracy will be calculated as (True Positives + True Negatives) / Total Samples "True Positive" will be when the AI correctly identifies a procedural phase or critical anatomic structure. "False Positive" will be when it incorrectly labels a procedural phase or critical anatomic structure as the phase or structure of interest. "True Negatives" will be when the AI model correctly labels the phase or structure as not the label of interest. Accuracy and precision will be determined for each of the following labels: each of three procedural phases (pre-, intra-, and post-gallbladder dissection) each of seven biliary structures: gallbladder, cystic duct, common hepatic duct, common bile duct, cystic artery, region of interest, danger zone |
Immediately after each procedure | |
Secondary | Length of procedure | Augmented display of the critical anatomic structures and procedural phases using indocyanine green (ICG) and laser speckle contrast imaging (LSCI) may detect the structures and procedural phases earlier than LC performed using standard WLI display alone | Immediately after each procedure | |
Secondary | Conversion rate to open procedure | Routine augmented visualization and identification of the critical phases and structures may potentially reduce current conversion rate. | Immediately after each procedure | |
Secondary | Complication rate | Comparison of complication rate between augmented visualization and WLI groups | One month after each procedure | |
Secondary | Length of hospital stay | Comparison of hospital stay length between augmented visualization and WLI groups | One month after each procedure |
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