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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04079478
Other study ID # 2363
Secondary ID
Status Completed
Phase
First received
Last updated
Start date September 25, 2019
Est. completion date December 31, 2019

Study information

Verified date February 2020
Source Istituto Clinico Humanitas
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Recruitment information / eligibility

Status Completed
Enrollment 700
Est. completion date December 31, 2019
Est. primary completion date December 31, 2019
Accepts healthy volunteers No
Gender All
Age group 40 Years to 80 Years
Eligibility 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


Related Conditions & MeSH terms


Intervention

Other:
AI
Artificial intellignece colonoscopy

Locations

Country Name City State
Italy Endoscopy Unit, Humanitas Research Hospital Rozzano Milano

Sponsors (1)

Lead Sponsor Collaborator
Istituto Clinico Humanitas

Country where clinical trial is conducted

Italy, 

Outcome

Type Measure Description Time frame Safety issue
Primary 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
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