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Deep Learning Algorithm for Recognition of Colonic Segments.

NCT04087824 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 60

Last updated 2019-09-12

No results posted yet for this study

Summary

The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

Conditions

  • Colonic Diseases

Interventions

DEVICE

AI assisted recognition of colonic segments

After receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. The whole withdrawal process is monitored by AI associated recognition system. Key colonic segments include ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. When typical anatomic sites are detected, the AI device will automatically captured relevant images and report the name of each segment on the screen. The operating endoscopy expert will give the final answer and judge the performance of AI, which is set as a golden standard. Then all the AI captured images will be reviewed by human group, which consists of three to five experienced endoscopic physicians.

Sponsors & Collaborators

  • Shandong University

    lead OTHER

Principal Investigators

  • Xiuli Zuo, MD,PhD · Qilu Hospital of Shandong University

Study Design

Allocation
NA
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
SINGLE_GROUP

Eligibility

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

Timeline & Regulatory

Start
2019-09-15
Primary Completion
2019-11-15
Completion
2019-12-15

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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 NCT04087824 on ClinicalTrials.gov