Impact of Artificial Intelligence (AI) on Adenoma Detection During Colonoscopy in FIT+ Patients. (AIFIT)
Impact of AI (Artificial Intelligence) on Adenoma Detection During Colonoscopy in FIT+ Patients: a Prospective Randomized Controlled Trial
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 Locations
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-
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Como, Italy, 22100
- Gastroenterology Unit, Valduce Hospital
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Consecutive adult (50-74 yrs.) outpatients undergoing colonoscopy in the frame of the FIT-based screening program.
Exclusion Criteria:
- patients with CRC history or hereditary polyposis syndromes or hereditary non-polyposis colorectal cancer
- patients with inadequate bowel preparation
- patients in which cecal intubation was not achieved or scheduled for partial examinations
- patients with gastrointestinal symptoms
- polyps could not be resected due to ongoing anticoagulation preventing resection and pathological assessment
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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No Intervention: Standard WL (white light) colonoscopy
all patients receive standard colonoscopy (with high definition- HD- endoscopes) with white light (WL) in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination.
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|
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Experimental: Standard colonoscopy with assistance of Artificial Intelligence (CAD-EYE (Fujifilm Co, Tokyo, Japan)
all patients receive colonoscopy examinations (with HD endoscopes) equipped with an Ai system (CAD-EYE, Fujifilm Co, Tokyo, Japan) in both insertion and withdrawal phase).
This system is a real-time computer-assisted image analysis that allows automatic polyp identification without modifications to the colonoscope or to the actual endoscopic procedure.
When CAD EYE identifies a polyp, both a visual (a green blinking box surrounding the identified polyp, called the detection box) and an acoustic alarm pop up and attract the endoscopist attention.
Around the endoscopic image a visual assist circle is shown and lights up in the direction where the suspicious polyp is detected.All polyps identified are removed and sent for histopathology examination.
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A dedicated CNN-based AI system (CAD EYE, Fujifilm Co, Tokyo, Japan) has been recently developed.
The Computer-aided diagnosis (CAD) CAD EYE system is a real-time computer-assisted image analysis that allows automatic polyp identification without modifications to the colonoscope or to the actual endoscopic procedure.
When CAD EYE identifies a polyp, both a visual (a green blinking box surrounding the identified polyp, called the detection box) and an acoustic alarm pop up and attract the endoscopist attention.
Around the endoscopic image a visual assist circle is shown and lights up in the direction where the suspicious polyp is detected.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
ADR
Time Frame: 10 months
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Adenoma Detection Rate: rate of participants with at least on adenoma detected during colonoscopy
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10 months
|
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APC
Time Frame: 10 months
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Adenoma per Colonoscopy: it is determined by dividing the total number of adenomas removed by the total number of colonoscopies performed
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10 months
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Adv-ADR
Time Frame: 10 months
|
Adv-ADR: rate of participants with at least on advanced adenoma detected during colonoscopy
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10 months
|
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SSL-DR:
Time Frame: 10 months
|
SSL-ADR: the serrated lesions with neoplastic potential (sessile serrated lesions-SSA; traditional serrated adenomas - TSA) detection rate.
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10 months
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Other Outcome Measures
Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Impact of Ai on endoscopist with different ADR
Time Frame: 10 months
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The variation in ADR will be stratified according the initial ADR of endoscopists participating in the present study
|
10 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
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 598/2020
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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