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Completed

NCT Number: NCT04691401

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

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.

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Key information

Age range

50 year–74 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Gastroenterology Unit, Valduce Hospital

Como, 22100, Italy

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. ADR

    Time frame: 10 months

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

  2. APC

    Time frame: 10 months

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

Secondary outcomes

  1. Adv-ADR

    Time frame: 10 months

    Adv-ADR: rate of participants with at least on advanced adenoma detected during colonoscopy

  2. SSL-DR:

    Time frame: 10 months

    SSL-ADR: the serrated lesions with neoplastic potential (sessile serrated lesions-SSA; traditional serrated adenomas - TSA) detection rate.

Other outcomes

  1. Impact of Ai on endoscopist with different ADR

    Time frame: 10 months

    The variation in ADR will be stratified according the initial ADR of endoscopists participating in the present study

Sponsors and collaborators

Lead sponsor

Valduce Hospital

Other

Registry information

Official study title

Impact of AI (Artificial Intelligence) on Adenoma Detection During Colonoscopy in FIT+ Patients: a Prospective Randomized Controlled Trial

Acronym: AIFIT

Important dates

Study start
2020
Primary completion
2021
Study completion
2021
First posted
Dec 31, 2020
Registry last updated
Mar 26, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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