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Completed

NCT Number: NCT03840590

Adenoma Detection Rate Using AI System in China

The primary aim of this study is

- to explore the usefulness of Artificial Intelligence system in colonoscopy on adenoma detection rate (ADR). Other aims include to explore the data below when Artificial Intelligence is used.

Mean adenomas detected per procedure, MAP Proximal Adenoma detection rate, pADR Polyp detection rate, PDR Proximal polyp detection rate, pPDR Mean polyps detected per procedure, MPP Withdrawal time, WT Cecal intubation rate, CIR Cecal intubation time, CIT

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Key information

Conditions

Colorectal Neoplasms Adenoma Colonic Diseases Colonic Polyp Colonic Polyps Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Intestinal Polyps Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Polyps Rectal Diseases

Age range

45 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Sixth affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China

Loading trial locations.

About this study

Colorectal cancer is common in China. Most colorectal cancers happen when an adenoma becomes cancerous. Doctors use colonoscopy to look inside the colon and rectum and find adenomas and remove them. Removing adenomas is known to reduce the chances of a person developing colorectal cancers. The ability of colonoscopists finding adenomas varies, and there is a lot of researches into how to improve "adenoma detection rates".

A new AI system, called the CSK endoscopic diagnosis and treatment system has been designed to improve the rate of polyp detection at colonoscopy. Previous tests have shown that there is a significant improvement in detection of adenomas when the system is used. This study will randomize patients coming for colonoscopy to have their procedure performed as usual or as an AI-assisted colonoscopy. The investigators will record polyp and adenoma detection rates, duration of procedure, participant comfort levels, and complications. All patients referred for colonoscopy will be invited in 4 centers, recruiting a total of 800 participants.

Who can participate

Healthy volunteers accepted: Yes

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

Inclusion criteria

  • All patients referred for screening, surveillance, or diagnostic colonoscopy
  • All patients must be able to give informed consent

Exclusion criteria

  • Patients with any absolute contraindications to colonoscopy
  • Patients with established or suspicion of large bowel obstruction or pseudo-obstruction
  • Patients with known colon cancer or polyposis syndromes
  • Patients with known colonic strictures
  • Patients with known severe diverticular segments (that is likely to impede colonoscope passage)
  • Patients with active colitis (ulcerative colitis, Crohn's colitis, diverticulitis, infective colitis)
  • Patients lacking capacity to give informed consent
  • Pregnancy
  • Patients who are on clopidogrel, warfarin, or other new generation anticoagulants who have not stopped this for the procedure.
  • Patients who are attending for a therapeutic procedure or assessment of a known lesion

Treatment and study plan

CSK AI system

Device

CSK system is equipped with the endoscopic mainframe and alarms the examiners when suspect lesions appear.

Standard Colonoscopy

Device

This is the routine examination in nowadays clinical practice. Both the equipment and the procedure are as usual as clinical routine.

Primary outcomes

  1. Adenoma detection rate, ADR

    Time frame: At the end of the procedure, up to 1 hour.

    ADR refer to the rate of adenoma detection, calculated as the proportion of subjects with at least one adenoma.

Secondary outcomes

  1. Polyp detection rate, PDR

    Time frame: At the end of the procedure, up to 1 hour.

    PDR refer to the rate of polyp detection, calculated as the proportion of subjects with at least one polyp.

Sponsors and collaborators

Lead sponsor

Changhai Hospital

Other

Collaborators

  • Shanghai Zhongshan Hospital
  • Sixth Affiliated Hospital, Sun Yat-sen University
  • Tianjin Nankai Hospital
  • Tianjin People's Hospital

Registry information

Official study title

Adenoma Detection Rate Using Artificial Intelligence System in China

Acronym: SinoAIADR

Important dates

Study start
2019
Primary completion
2019
Study completion
2019
First posted
Feb 15, 2019
Registry last updated
Sep 25, 2020

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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