Impact of Artificial Intelligence (AI) on Adenoma Detection During Colonoscopy in FIT+ Patients.

NCT04691401 | Completed

• 1 other identifier: 598/2020
• Study Type: interventional
• Target: 750
• Locations: 1 country
• Sites: 1

Brief Summary

The Italian screening program invites the resident population aged 50-74 for Fecal Immunochemical Test (FIT) every 2 years. Subjects who test positive are referred for colonoscopy. Maximizing adenoma detection during colonoscopy is of paramount importance in the framework of an organized screening program, in which colonoscopy represent the key examination. Initial studies consistently show that Artificial iIntelligence-based systems support the endoscopist in evaluating colonoscopy images potentially increasing the identification of colonic polyps. However, the studies on AI and polyp detection performed so far are mostly focused on technical issues, are based on still images analysis or recorded video segments and includes patients with different indications for colonoscopy. At the best of our knowledge, data on the impact on AI system in adenoma detection in a FIT-based screening program are lacking. The present prospective randomized controlled trial is aimed at evaluating whether the use of an AI system increases the ADR (per patient analysis) and/or the mean number of adenomas per colonoscopy in FIT-positive subjects undergoing screening colonoscopy. Therefore Patients fulfilling the inclusion criteria are randomized (1:1) in two arms: A) 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; B) patients receive colonoscopy examinations (with HD endoscopes) equipped with an AI system (in both insertion and withdrawal phase); all polyps identified are removed and sent for histopathology examination. In the present study histopathology represents the reference standard.

Conditions

Polyp of Colon

Keywords

adenoma detection

Outcome Measures

Primary Outcomes (2)

• ADR

  Adenoma Detection Rate: rate of participants with at least on adenoma detected during colonoscopy

  10 months

• APC

  Adenoma per Colonoscopy: it is determined by dividing the total number of adenomas removed by the total number of colonoscopies performed

  10 months

Secondary Outcomes (2)

• Adv-ADR

  10 months

• SSL-DR:

  10 months

Other Outcomes (1)

• Impact of Ai on endoscopist with different ADR

  10 months

Study Arms (2)

Standard WL (white light) colonoscopy

NO INTERVENTION

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.

Standard colonoscopy with assistance of Artificial Intelligence (CAD-EYE (Fujifilm Co, Tokyo, Japan)

EXPERIMENTAL

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.

Device: Artificial Intelligence System (CAD EYE, Fujifilm Co.)

Interventions

Artificial Intelligence System (CAD EYE, Fujifilm Co.) DEVICE

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.

Standard colonoscopy with assistance of Artificial Intelligence (CAD-EYE (Fujifilm Co, Tokyo, Japan)

Eligibility Criteria

• Age: 50 Years - 74 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Consecutive adult (50-74 yrs.) outpatients undergoing colonoscopy in the frame of the FIT-based screening program.

You may not qualify if:

• 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

Sponsors & Collaborators

• Valduce Hospital: lead

Study Sites (1)

Gastroenterology Unit, Valduce Hospital

Como, 22100, Italy

Location

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Gastroenterology Unit

Study Record Dates

• First Submitted: December 17, 2020
• First Posted: December 31, 2020
• Study Start: December 20, 2020
• Primary Completion: October 31, 2021
• Study Completion: December 31, 2021
• Last Updated: March 26, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)