The AID Study: Artificial Intelligence for Colorectal Adenoma Detection

NCT ID: NCT04079478

Last Updated: 2020-02-12

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 700 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-09-25
• Study Completion Date: 2019-12-31

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

AI

Artificial Intelligence colonoscopy

AI

Intervention Type: OTHER

Artificial intellignece colonoscopy

Control

White light colonoscopy

No interventions assigned to this group

Interventions

AI

Artificial intellignece colonoscopy

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• 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.

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Istituto Clinico Humanitas

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Alessandro Repici, MD

Role: PRINCIPAL_INVESTIGATOR

Humanitas Research Hospital IRCCS, Rozzano-Milan

Locations

Endoscopy Unit, Humanitas Research Hospital

Rozzano, Milano, Italy

Countries

Italy

Other Identifiers

2363

Identifier Type: -

Identifier Source: org_study_id