The AID Study: Artificial Intelligence for Colorectal Adenoma Detection

NCT04079478 | Completed

• 1 other identifier: 2363
• Study Type: observational
• Target: 700
• Locations: 1 country
• Sites: 1

Brief Summary

Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (\< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC8. Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.

Conditions

Colon Cancer

Keywords

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Additional diagnostic yield obtained by AI-aided colonoscopy to the yield obtained by the Standard (high-definition) colonoscopy

  To compare the additional diagnostic yield obtained by AI-aided colonoscopy to the yield obtained by the Standard (high-definition) colonoscopy

  3 Months

Study Arms (2)

AI

Artificial Intelligence colonoscopy

Other: AI

Control

White light colonoscopy

Interventions

AI OTHER

Artificial intellignece colonoscopy

AI

Eligibility Criteria

• Age: 40 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Based on the observed prevalence of adenomas (35%) among patients undergoing colonoscopies at our center within the last 12 months, a sample size of 322 subjects per arm could allow for a 90% power to show the non-inferiority (primary end-point) of the AI-aided arm by excluding that the one-side 95% CI will exclude a difference of 10% in favour of the standard group. Such sample size will also have a 80% power to detect as statistical significant (α=0.05; two-sided test) a 10% absolute increase in the detection rate of adenomas in the AI-aided arm (secondary end-point).

You may qualify if:

• All 40-80 years-old subjects undergoing a colonoscopy.

You may not qualify if:

• subjects with personal history of CRC, or IBD.
• patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \> 2 in any colonic segment).
• patients with previous colonic resection.
• patients on antithrombotic therapy, precluding polyp resection.
• patients who were not able or refused to give informed written consent.

Sponsors & Collaborators

• Istituto Clinico Humanitas: lead

Study Sites (1)

Endoscopy Unit, Humanitas Research Hospital

Rozzano, Milano, 20089, Italy

Location

MeSH Terms

Conditions

Colonic Neoplasms

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases

Study Officials

• Alessandro Repici, MD

  Humanitas Research Hospital IRCCS, Rozzano-Milan

  PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

• First Submitted: September 3, 2019
• First Posted: September 6, 2019
• Study Start: September 25, 2019
• Primary Completion: December 31, 2019
• Study Completion: December 31, 2019
• Last Updated: February 12, 2020

Record last verified: 2020-02

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)