ICG for Gallbladder Disorders

Overseen By Christopher McCulloh, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Activ Surgical

Must not be taking: Investigational agents

Disqualifiers: Pregnancy, Liver disease, Renal dysfunction, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 4

120 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests an AI tool designed to help surgeons better identify structures and phases during gallbladder surgery. The AI will analyze video from the surgery without altering what the surgeon sees or does. The trial compares surgeries using a dye called indocyanine green (ICG) with those that do not use it. Individuals with gallbladder issues, such as gallstones or inflammation, who are scheduled for surgery might be suitable candidates. As an unphased trial, this study allows patients to contribute to innovative surgical advancements without the constraints of traditional clinical trial phases.

Will I have to stop taking my current medications?

The trial protocol does not specify whether you need to stop taking your current medications. However, if you are on any investigational agents, you would not be eligible to participate.

What prior data suggests that this AI model is safe for use in surgery?

Studies have shown that indocyanine green (ICG) is generally safe for use in surgeries. ICG enhances surgeons' ability to see bile ducts clearly during operations. It quickly absorbs and releases into the bile, highlighting the anatomy.

Research indicates that patients tolerate ICG well. For example, one study found that using ICG in surgeries improved visibility of the bile ducts without causing additional harm. Another study demonstrated that ICG can also make surgeries faster and more successful.

The FDA has already approved ICG for this use, which adds confidence in its safety. Although no treatment is without risks, current findings suggest that ICG is a safe choice for improving surgical outcomes. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the use of indocyanine green (ICG) in gallbladder surgeries because it offers a new way to visualize the biliary anatomy during the procedure. Unlike standard methods, which rely on the surgeon's direct view and experience, ICG is injected intravenously and allows for enhanced imaging through a special device called ActivSight. This technology, combined with ActivInsight’s real-time AI analysis of surgical videos, potentially increases the precision of identifying important anatomical structures. This innovative approach could lead to safer surgeries and better outcomes for patients with gallbladder disorders.

What evidence suggests that this trial's treatments could be effective for gallbladder disorders?

Research has shown that Indocyanine Green (ICG), which participants in this trial may receive, enhances visibility of the bile system (the organs and tubes involved in making and moving bile) during surgery. Studies have found that ICG can speed up gallbladder surgery and help surgeons identify critical structures more effectively. ICG highlights the bile ducts when viewed with a special camera, improving visibility during the operation. It is considered safer and faster than traditional methods for visualizing the bile ducts. These findings support its use in surgeries related to gallbladder issues. ^{1 2 6 7 8}

Who Is on the Research Team?

Peter Kim, MD, PhD

Principal Investigator

Activ Surgical

Are You a Good Fit for This Trial?

Adults over 18 needing elective gallbladder removal due to conditions like gallstones can join. They must understand the local language, be able to follow study procedures, and have normal organ function tests. Excluded are those with allergies to ICG dye, liver disease, coagulopathy, pregnant or breastfeeding women, and anyone unable to consent.

Inclusion Criteria

My liver function tests are within normal ranges.

I don't have trouble breathing at rest and my oxygen level is above 94%.

Your platelet count is at least 75,000 per microliter, and if it's lower, you may receive a transfusion.

Exclusion Criteria

Pregnant women

Prisoners

Pregnancy or breast-feeding

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Pre-dissection

Surgeons perform the procedure in their standard practice while AI analyzes data from the laparoscopic camera to identify structures and phases before dissection.

Immediately before surgery

1 visit (in-person)

Intra-dissection

AI/ML model analyzes surgical video in real time to identify anatomic structures and phases during dissection.

During surgery

1 visit (in-person)

Post-dissection

AI/ML model continues to analyze surgical video to ensure all critical structures are identified after dissection.

Immediately after surgery

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after the procedure.

4 weeks

1 visit (in-person)

What Are the Treatments Tested in This Trial?

Interventions

• AI Model

Trial Overview

The trial is testing an AI model's ability to identify surgical structures and phases during laparoscopic cholecystectomy against traditional methods by trained surgeons. Surgeons will operate as usual while the AI analyzes video data without altering their view.

How Is the Trial Designed?

Group I: Indocyanine green (ICG) Experimental Treatment 1 Intervention

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.

Group II: Non-Indocyanine Green (Non-ICG) Active Control 1 Intervention

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.

Who Is Running the Clinical Trial?

Activ Surgical

Lead Sponsor

Trials

4

Recruited

260+

Published Research Related to This Trial

A Danish initiative has successfully developed computer-assisted decision support tools that significantly reduce the rates of perforated appendices and negative laparotomies in acute abdomen cases.

In jaundice diagnosis, three statistical methods have been tested to effectively identify causes, demonstrating the potential to integrate ultrasonographic data into clinical assessments, paving the way for improved diagnostic accuracy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-assisted diagnosis in gastroenterology. Malchow-Møller, A., Bjerregaard, B., Hilden, J.[2019]

The GoNoGoNet AI algorithm was able to identify unsafe dissection zones during laparoscopic cholecystectomy, showing that a significant proportion of dissection interactions in cases of bile duct injury occurred outside the predicted safe zones.

Injury-causing interactions were found in both the No-Go and Go zones, indicating that while the AI can help identify dangerous areas, further optimization is needed for its effective integration into surgical practice to prevent bile duct injuries.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy. Khalid, MU., Laplante, S., Masino, C., et al.[2023]

The newly developed care bundle for managing acute biliary pancreatitis includes 7 key elements, emphasizing the avoidance of antibiotic prophylaxis and recommending a full-solid diet for patients with mild to moderate cases.

The bundle also highlights the importance of timely interventions, such as performing endoscopic retrograde cholangiopancreatography within 48 to 72 hours for patients with cholangitis and recommending early laparoscopic cholecystectomy for mild cases, enhancing patient care through evidence-based practices supported by AI analysis.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The 2023 MANCTRA Acute Biliary Pancreatitis Care Bundle: A Joint Effort Between Human Knowledge and Artificial Intelligence (ChatGPT) to Optimize the Care of Patients With Acute Biliary Pancreatitis in Western Countries. Podda, M., Di Martino, M., Ielpo, B., et al.[2023]

A new laparoscopic surgical system using Artificial Intelligence (AI) has been developed to accurately identify key anatomical landmarks during laparoscopic cholecystectomy, aiming to prevent intraoperative bile duct injuries.

The AI system has shown high accuracy in clinical tests, and while it has the potential to support surgeons and reduce complications, challenges remain in adapting it to various surgical conditions, such as inflammation of the gallbladder.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of Endoscopic Surgery Navigated by Artificial Intelligence. Inomata, M., Fujinaga, A., Nakanuma, H., et al.[2021]

A study of 748 patients with iatrogenic bile duct injury (IBDI) found that machine learning models can accurately predict the success of initial repairs, achieving an accuracy of 82.8%.

Factors such as non-type E injuries, treatment at specialized centers, and surgical repair significantly improve prognosis, highlighting the importance of timely and appropriate management in IBDI cases.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study. Lopez-Lopez, V., Maupoey, J., López-Andujar, R., et al.[2022]

Artificial intelligence (AI) has the potential to revolutionize gastroenterology by rapidly and accurately analyzing clinical data, which could surpass human specialists in certain diagnostic tasks.

Current research is focused on using AI for tasks such as interpreting colonoscopy results, enhancing the detection of polyps, and predicting patient responses to treatments, indicating a promising future for AI in improving patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology. Stidham, RW.[2022]

An artificial intelligence framework was developed to classify the vascularity of the gallbladder wall during laparoscopic cholecystectomy, achieving high accuracy rates of 92.6% to 93.2% and F1 scores of 93.5% to 93.9% based on analysis of 234 gallbladder images.

The AI model's performance closely matched that of expert evaluators, indicating its potential as a reliable tool for enhancing surgical decision-making regarding gallbladder vascular patterns.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multiple instance convolutional neural network for gallbladder assessment from laparoscopic images. Loukas, C., Gazis, A., Schizas, D.[2022]

Artificial intelligence, particularly artificial neural networks, is revolutionizing gastroenterology by enhancing the analysis of endoscopy and imaging data, allowing for better detection of subtle patterns in medical images.

AI not only aids in identifying and classifying pathological findings but also has the potential to predict clinical outcomes based on these subtle image features, improving patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Artificial intelligence in gastroenterology]. Kather, JN., Krause, J., Luedde, T.[2021]

AI models developed for laparoscopic cholecystectomy can effectively identify safe (Go) and dangerous (No-Go) zones of dissection, achieving high accuracy and specificity, which may help reduce surgical errors.

The models were trained on a large dataset of 290 videos from 37 countries, demonstrating their potential for real-time guidance during surgery to enhance safety and minimize adverse events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence for Intraoperative Guidance: Using Semantic Segmentation to Identify Surgical Anatomy During Laparoscopic Cholecystectomy. Madani, A., Namazi, B., Altieri, MS., et al.[2023]

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