Intelligent-C Endoscopy Module for Real-time Detection of Colonic Lesions

NCT05784935 | Completed

• 1 other identifier: iIDEAS/RTD
• Study Type: observational
• Target: 380
• Locations: 1 country
• Sites: 1

Brief Summary

To conduct an single blinded, non-randomized, prospective, single center trial to validate the performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred for a screening, surveillance or diagnostic colonoscopy will be included

Conditions

Artificial Intelligence; Colonic Polyp

Outcome Measures

Primary Outcomes (1)

• Performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy

  A single-blinded, non-randomized prospective trial to validate the performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy.

  1 Year

Interventions

Real time AI detection model DEVICE

Bowel preparation will be conducted according to usual local practices. During colonoscopy, the colonoscope will be first advanced to the cecum in all patients as confirmed by identification of the appendicular orifice and ileocecal valve or by intubation of the ileum, as per the standard of care by experienced endoscopists. During the insertion, no action will be taken. After cecal intubation is performed, the colonoscope will be slowly withdrawn to the splenic flexure by the primary endoscopists. Real time AI detection model will be activated with the output displayed in real time on a separate monitor and will be only viewed by an independent investigator, who is an experienced endoscopists (or a person trained in polyp recognition). The primary endoscopists will be blinded to the AI real time detection result

Eligibility Criteria

• Age: 19 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Our pilot series showed AI assistance can detect up to 80 % of missed lesions with sample variance at around 0.032. We hypothesize that the adenoma/polyp miss rate of conventional colonoscopy can be reduced by 50% to 75% with AI assistance. Assuming 4% patients may be excluded. The sample size is estimated to be 381 patients in total with a power of 95% and a significance level of 0.05. An interim analysis will be performed when the first 150 patients are recruited to verify the sample size estimation

You may qualify if:

• Patients undergoing colonoscopy of age 18 to 80 years.
• Consecutive adult patients scheduled to undergo routine colonoscopy at AIG

You may not qualify if:

• Females who are pregnant
• Patients who are unsuitable for any other reason to participate in the study in the opinion of the investigator
• Recent colonoscopy within past 12 month
• History of inflammatory bowel disease
• History of colorectal cancer
• Previous bowel resection (apart from appendectomy)
• Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
• Bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.

Sponsors & Collaborators

• Asian Institute of Gastroenterology, India: lead

Study Sites (1)

AIG Hospitals

Hyderabad, Telangana, 500032, India

Location

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Officials

• Hardik Rughwani, MD, DM

  Asian Institute of Gastroenterology

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 13, 2022
• First Posted: March 27, 2023
• Study Start: March 20, 2023
• Primary Completion: July 1, 2023
• Study Completion: November 1, 2023
• Last Updated: January 27, 2025

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will not share

Locations

IN (1)