The EYE Study Enhancing the Diagnostic Yield of Standard Colonoscopy by Artificial Intelligence Aided Endoscopy (EYE)
The EYE Study: Enhancing the Diagnostic Yield of Standard Colonoscopy by Artificial Intelligence Aided Endoscopy
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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Alessandro Repici, MD
- Phone Number: 0039-02-82247493
- Email: alessandro.repici@humanitas.it
Study Locations
-
-
Milano
-
Rozzano, Milano, Italy, 20089
- Department of Gastroenterology, Humanitas Research Hospital
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- 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).
Exclusion Criteria:
- 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.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: WL+AI
Colonoscopy in white light and artificial intelligence
|
Artificial intelligence
|
|
Experimental: WL
Colonoscopy in white light
|
Artificial intelligence
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Adenoma per colonoscopy (APC)
Time Frame: 9 Months
|
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 Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Positive predictive value (PPV)
Time Frame: 9 Months
|
PPV, defined as the total number of histologically confirmed adenomas and carcinomas detected during the colonoscopy, divided by the total number of excisions in the colonoscopy.
|
9 Months
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- 3000
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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