MedTrace Logo

Artificial Intelligence Identifying Polyps in Real-world Colonoscopy

NCT03761771 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 209

Last updated 2018-12-17

No results posted yet for this study

Summary

Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown. Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.

Conditions

  • Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy
  • Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS

Interventions

DEVICE

colonoscopy withdrawal with the ADS monitoring

During the testing of trained ADS, when the system doubts colonic lesions from the input data of the test images, a rectangular frame was displayed in the endoscopic image to surround the lesion. If the system confirmed it as the colonic lesions, a sound of reminder will be played and the types of lesions (non-adenomatous polyps, adenomatous polyps and colorectal cancers) will be classified by the system. We adopted several standards to define the identification and classification of colonic lesions: 1) when the system identified and confirmed any lesion in the images of no polyps or cancers, the results were judged to be false-positive. 2) when the system both confirmed and correctly localized the lesions in images (IoU \> 0.3), the results were judged to be true-positive. 3) when the system did not confirm or correctly localize the lesions, the results were judged as false-negative. 4) when system confirmed no lesions in the normal images, the results were judged to be true-negative.

Sponsors & Collaborators

  • Zhaoshen Li

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-11-01
Primary Completion
2018-12-10
Completion
2018-12-10

Countries

  • China

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03761771 on ClinicalTrials.gov