The EYE Study Enhancing the Diagnostic Yield of Standard Colonoscopy by Artificial Intelligence Aided Endoscopy

NCT05139186 | Completed

• 1 other identifier: 3000
• Study Type: interventional
• Target: 1,120
• Locations: 1 country
• Sites: 1

Brief Summary

Colorectal cancer (CRC) remains one of the leading causes of mortality among neoplastic diseases in the world\[1\] . Adequate colonoscopy based CRC screening programs have proved to be the key to reduce the risk of mortality, by early diagnosis of existing CRC and detection of pre-cancerous lesions\[2-4\] . Nevertheless, long-term effectiveness of colonoscopy is influenced by a range of variables that make it far from a perfect tool\[5\]. The effectiveness of a colonoscopy mainly depends on its quality, which in turn is dependent on the skill and expertise of the endoscopist. In fact, several studies have shown a significant adenoma miss rate of 24%-35%, especially in patients with diminutive adenomas\[6,7\] . These data are in line with interval cancers incidence (I-CRC), defined as the percentage of cancers diagnosed after a screening program and before the intended surveillance duration, of approximately 3%-5% \[8,9\]. The development of the artificial intelligence (AI) applications in the medical field has grown in interest in the past decade. Its performance on increasing automatic polyp and adenoma detection has shown promising results in order to achieve an higher ADR\[10\]. The use of computer aided diagnosis (CAD) for detection of polyps had initially been studied in ex vivo studies but in the last few years, with the advancement in computer aided technology and emergence of deep learning algorithms, use of AI during colonoscopy has been achieved and more studies have been undertaken \[10\]. Recently Fujifilm (Tokyo, Japan) has developed a new technology known as "CAD-EYE" aiming to support both colonic polyp detection and characterization during colonoscopy. This technology is now available in Europe, being compatible with the latest generation of Fujifilm endoscopes (ELUXEO Fujifilm Co.). However, the clinical impact of CAD-EYE system in improving the adenoma detection have yet to be assessed

Conditions

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Adenoma per colonoscopy (APC)

  APC, defined as the total number of histologically confirmed adenomas and carcinomas detected in the colonoscopy divided by the total number of colonoscopies.

  9 Months

Secondary Outcomes (1)

• Positive predictive value (PPV)

  9 Months

Study Arms (2)

WL+AI

EXPERIMENTAL

Colonoscopy in white light and artificial intelligence

Device: Artificial Intelligence

WL

EXPERIMENTAL

Colonoscopy in white light

Device: Artificial Intelligence

Interventions

Artificial Intelligence DEVICE

Artificial intelligence

WL; WL+AI

Eligibility Criteria

• Age: 45 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• \- patients aged 45 or older undergoing average risk colonoscopy (screening) or follow-up colonoscopy for previous history of polyps (surveillance interval of 3 years or greater).

You may not qualify if:

• subjects with personal history of CRC, or IBD.
• subjects affected with Lynch syndrome or Familiar Adenomatous Polyposis.
• 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)

Department of Gastroenterology, Humanitas Research Hospital

Rozzano, Milano, 20089, Italy

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 18, 2021
• First Posted: December 1, 2021
• Study Start: January 1, 2022
• Primary Completion: December 31, 2022
• Study Completion: October 10, 2023
• Last Updated: August 6, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)