NCT06621225

Artificial Intelligence in Colonoscopy

A Randomized Trial on the Utilization of Endocuff-assisted Colonoscopy and Computer-aided Detection in Optimizing Colonoscopies in the Elderly

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 264 (actual)
• Sponsor: Pankaj Patel · Academic / Other
• Sex: All
• Age: 75 Years
• Healthy volunteers: Accepted

Summary

N = 264 patients (50% female) aged 75 years and above undergoing colonoscopy were enrolled. Patients were randomly assigned into one of the three intervention groups: the primary intervention arm (CADe in combination with the MED), the second group with MED alone, and the control group with WLE. All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was the adenoma detection rate. Secondary outcomes were adenoma detection in the left colon in our cohort of patients.

Detailed description

264 patients, with an equal gender distribution (50% female), aged 75 years and above, undergoing screening and diagnostic colonoscopy at The Surgery and Endoscopy Center of Sebring, Sebring, Florida, were enrolled in this study. The eligibility criteria for a randomized controlled trial (RCT) comparing AI and mucosal exposure devices together, mucosal exposure devices alone, and white light endoscopy in patients 75 years and older could be structured as follows. Patients were randomly allocated into one of three study groups: the primary intervention arm, where colonoscopy was performed using the CADe in combination with the MED; the second group underwent colonoscopy solely with the MED, while the control group underwent colonoscopy solely with the WLE. We used a Convolutional Neural Network-based CADe system, GI Genius, acquired for licensed use from Medtronic Inc., Minneapolis, MN. The MED employed was the EndoCuff Vision (ECV) developed by Olympus America, Center Valley, PA, which constitutes part of the standard equipment available. All detected lesions were identified and excised throughout the colonoscopy procedures, and specimens were promptly sent for histopathological analysis. The primary outcome of interest was adenoma detection rate (ADR), defined as the percentage of patients in whom at least one histologically proven adenoma or carcinoma was identified during colonoscopy. Secondary outcomes included ADR in the left colon in our cohort of patients. This study was conducted according to accepted ethical principles and approved by the institutional review board (IRB) of "The Surgery and Endoscopy Center of Sebring.\" Informed consent was obtained from all participants before enrollment, and measures were taken to ensure patient confidentiality and data protection throughout the study period.

Conditions

• Colorectal Carcinoma

Interventions

Type	Name	Description
DEVICE	Artificial Intelligence	Medtronic GI Genius is an advanced AI-powered platform designed to assist gastroenterologists during colonoscopies. Utilizing deep learning algorithms, it analyzes real-time endoscopic images to detect and highlight polyps and other abnormalities, enhancing the detection rate and accuracy. The system provides visual cues to guide physicians in identifying potentially problematic areas that might be missed by the human eye alone. This technology aims to improve diagnostic precision, reduce missed detections, and ultimately enhance patient outcomes by facilitating earlier and more accurate interventions. GI Genius integrates seamlessly with existing endoscopy equipment, offering a valuable tool in the fight against colorectal cancer.
DEVICE	Mucosal Exposure Device	The Olympus Endocuff is an innovative device designed to enhance the effectiveness of colonoscopy procedures. It is a soft, flexible cuff that attaches to the end of the colonoscope and features multiple protruding \"fingers\" that help to improve mucosal exposure. By providing better visibility and maneuverability, the Endocuff helps gastroenterologists navigate and inspect the colon more thoroughly. It aids in the detection of polyps and other abnormalities by flattening folds and improving the overall view of the colon lining. This enhanced visualization contributes to more accurate diagnoses and can potentially reduce the miss rate of significant lesions, ultimately leading to better patient outcomes.

Timeline

• Start date: 2022-11-22
• Primary completion: 2023-05-31
• Completion: 2023-06-30
• First posted: 2024-10-01
• Last updated: 2024-10-01

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study