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

Study Overview
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Brief Summary
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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.

Detailed Description
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Bile duct injury (BDI) during cholecystectomy is a serious surgical complication with increased risk of early death, serious ongoing morbidities including multiple reinterventions requiring prolonged and repeated hospital stay, and over a billion dollars in additional healthcare costs in US each year. The introduction laparoscopic approach has progressively increased overall laparoscopic cholecystectomy procedure volume, for clearly proven benefits of minimally invasive approach. However, despite the benefits, minimally invasive approach has resulted in increased and persistent incidence of BDI up to 4-folds in some reports. Most major biliary injuries result from unrecognized or unintended perception, either misidentification or misinterpretation of the common bile or hepatic duct as the cystic duct or misidentification of an aberrant bile duct. It is increasingly clear that the routine use of intraoperative cholangiography (IOC) has a significant association with decreased and earlier intraoperative detection of BDI. The recent 'state of the art consensus conference on prevention of bile duct injury during cholecystectomy' in 2018 strongly recommended that documenting the critical view of safety (CVoS) and a liberal use of IOC in anticipated difficult cholecystectomy are highly recommended steps that can potentially mitigate the risk of BDI during laparoscopic cholecystectomy (LC) in patients especially with uncertain anatomy or difficult dissection. Performing traditional IOC laparoscopically under fluoroscopic guidance however can challenge surgeons' skills, is time-consuming, and requires a learning curve to interpret images. Despite well-known and accepted risk factors for difficult cholecystectomies and potential for BDI, the use of advanced imaging for IOC however remains variable and highly user dependent. The rationale for selective use of IOC is attributable to the fact that the prevalence and incidence of BDI are low that an individual practitioner would infrequently encounter such complication in their daily practice or lifetime of a surgeon to justify a routine use. This is further compounded by the fact that radiation-based fluoroscopic IOC are cumbersome to efficient workflow, utility and cost (need for contrast reagent preparation and injection, potential adverse allergic reactions, risk of radiation exposure, training requirement, need for large capital equipment, space and cost), and variability in proficient analysis. Recent randomized controlled trials using near-infrared fluorescent cholangiography (NIFC) using indocyanine green (ICG) demonstrated significantly superior visualization of extrahepatic biliary structures during laparoscopic cholecystectomy to white light (WLI) alone. Pre-dissection surgeon detection rates on naked eye were significantly higher (> 1.8 - 3.1 folds) with NIFC use for all 7 biliary structures than traditional WLI alone. However, although similar intergroup differences were observed for all structures, addition of NIFC did not improve additional detection of cystic duct and cystic duct/gallbladder junction after dissection has been done. In addition, increased body mass index was associated with reduced detection of most structures in both groups, especially before dissection. Interestingly, only 2 patients, both in the WLI group, sustained a biliary duct injury. ActivSightTM is an FDA-cleared device that combines ICG fluorescence for extrahepatic biliary visualization and laser speckle contrast imaging (LSCI) for perfusion detection in a laparoscopic form factor. ActivSightTM allows augmented visualization to any current WLI laparoscopic visualization system displaying both extrahepatic biliary ICG and microperfusion over cystic duct and artery. As a non-significant risk device, ActivSightTM has been used in well over 150 patients for laparoscopic cholecystectomies and bariatric, esophageal, and colorectal procedures, with proven safety and utility. Moreover, ActivSightTM allows raw infrared visualization data for advanced analysis and AI/ML model development. Surgeons/scientists now have segmented and analyzed different procedural phases of LC, and developed inferencing models and algorithms using artificial intelligence (AI)/machine learning (ML) based on standard WLI procedural videos. Although AI/ML models recognizing different phases of LC procedures can be as accurate as 80-95% on limited trained dataset once structures have been clearly dissected, more relevant and key value would be in identifying critical structures such as bile ducts and arteries before and during, not after surgical dissection has been performed. Early detection and identification of these critical structures before and during dissection of the triangle based on AI/ML trained on computer vision may aid surgeons in performing more effective and safer LC. ActivSightTM is an FDA 510(k)-cleared optical imaging system based on monochromatic coherent light known as Laser-speckle-contrast imaging (LSCI) and represents a label-free imaging method using coherent monochromatic light where blood flow and tissue perfusion can be detected. A small imaging module that fits between any existing laparoscope and camera systems and a separate light source placed along any current commercial system will deliver objective real-time tissue perfusion and blood flow information intraoperatively. In addition, ActivSightTM can also effectively display with push of button an ICG-based visualization of the biliary tree in real time at equivalent or superior to current commercial products. The innovative form factor of ActivSight enables any laparoscopic system for ICG-based visualization at a fraction of the cost of current competitor with minimal disruption to workflow. ActivInsightTM is a prototype software feature in ActivSightTM that recognizes procedural phases and critical anatomic structures, namely gallbladder, cystic duct, and cystic artery during LC using AI/ML-based algorithms. The key difference of ActivInsightTM to other models reported in the literature is that ActivInsightTM is trained on dataset annotated using ICG and LSCI in addition to WLI. In this trial, the investigators propose to first validate and then test the precision and accuracy of ActivInsightTM in detecting critical phases and structures and compare the performance of the algorithms to those based on models developed using WLI only and traditional surgeon's naked eye detection during LC in pre-and mid-dissection phases. To eliminate any bias, the investigators will connect a secondary screen blinded to the main operating surgeon screens so that real-time function of the AI/ML is not visible to operating surgeons, to perform real-time analysis and comparison of routine use of advanced augmented visualization versus current WLI visualization alone with or without computer vision-based AI/ML.

Official Title
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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

Conditions
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Cholecystitis, Cholelithiasis, Biliary Dyskinesia

Intervention / Treatment
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* Device: ICG


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Indocyanine green (ICG)<br>Patients in this arm will receive an intravenous injection of indocyanine green (ICG) 45 minutes prior to the start of surgery. This will be used to visualize the biliary anatomy using ActivSight, a device that is FDA 510(k)-cleared for this indication. The surgeon will perform the procedure in their standard fashion using ActivSight. ActivInsight artificial intelligence will be used to analyze the surgical video in real time to identify anatomic structures and phases of surgery. | Device: ICG<br>* Patient will receive a standard dose of indocyanine green (ICG) 45 minutes prior to surgery. ActivSight will be used during laparoscopic cholecystectomy - the surgeon will perform the procedure in their standard fashion. Video data will be analyzed in real-time by Activ Surgical's ActivInsight surgical artificial intelligence platform to identify anatomic structures and surgical phase. The surgeon will be blinded to this information, and will see the standard screens and information they do when performing the procedure in a non-study setting.<br>|
| No Intervention: Non-Indocyanine Green (Non-ICG)<br>The surgeon will perform the procedure in their standard fashion without the use of ICG. ActivInsight artificial intelligence will be used to analyze the surgical video in real time to identify anatomic structures and phases of surgery. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| Complication rate | Comparison of complication rate between augmented visualization and WLI groups | One month after each procedure | 
| Length of hospital stay | Comparison of hospital stay length between augmented visualization and WLI groups | One month after each procedure | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence, laparoscopy, laparoscopic surgery