The AID Study: Artificial Intelligence for Colorectal Adenoma Detection

Completed

• Conditions: Colon Cancer
• Interventions: Other: AI

• Registration Number: NCT04079478
• Lead Sponsor: Istituto Clinico Humanitas

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2019-09-03
• Study First Submitted QC: 2019-09-03
• Study First Posted: 2019-09-06
• Study Start: 2019-09-25
• Primary Completion: 2019-12-31
• Study Completion: 2019-12-31
• Status Verified: 2020-02
• Last Update Submitted: 2020-02-11
• Last Update Posted: 2020-02-12

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 700

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
AI	AI	Artificial Intelligence colonoscopy

Primary Outcome Measures

Name	Time	Method
Additional diagnostic yield obtained by AI-aided colonoscopy to the yield obtained by the Standard (high-definition) colonoscopy	3 Months	To compare the additional diagnostic yield obtained by AI-aided colonoscopy to the yield obtained by the Standard (high-definition) colonoscopy

Trial Locations

Locations (1)

Endoscopy Unit, Humanitas Research Hospital; 🇮🇹 Rozzano, Milano, Italy