Deep Learning Algorithm for Recognition of Colonic Segments.

NCT04087824 | Unknown DMC

• 1 other identifier: 2019SDU-QILU-G003
• Study Type: interventional
• Target: 60
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

Deep learning; Colonoscopy; Central Neural Networks

Outcome Measures

Primary Outcomes (1)

• The accuracy of each colonic segment real-time recognition with deep learning algorithm.

  The segmental recognition accuracy is the proportion of correctly recognized segments divided by the number of involved patients. The accuracy rate of ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum will be separately calculated.

  3 months.

Secondary Outcomes (1)

• The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.

  3 months.

Study Arms (1)

AI monitoring colonoscopy

EXPERIMENTAL

Patients in this group go through colonoscopy under the AI monitoring device.

Device: AI assisted recognition of colonic segments

Interventions

AI assisted recognition of colonic segments DEVICE

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.

AI monitoring colonoscopy

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients aged 18-70 years undergoing conventional colonoscopy

You may not qualify if:

• Known or suspected bowel obstruction, stricture or perforation
• Compromised swallowing reflex or mental status
• Severe chronic renal failure(creatinine clearance \< 30 ml/min)
• Severe congestive heart failure (New York Heart Association class III or IV)
• Uncontrolled hypertension (systolic blood pressure \> 170 mm Hg, diastolic blood pressure \> 100 mm Hg)
• Dehydration
• Disturbance of electrolytes
• Pregnancy or lactation
• Hemodynamically unstable
• Unable to give informed consent

Sponsors & Collaborators

• Shandong University: lead

MeSH Terms

Conditions

Colonic Diseases

Condition Hierarchy (Ancestors)

Intestinal Diseases; Gastrointestinal Diseases; Digestive System Diseases

Study Officials

• Xiuli Zuo, MD,PhD

  Qilu Hospital of Shandong University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiuli Zuo, MD,PhD

CONTACT

15588818685; zuoxiuli@sdu.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: director of Qilu Hospital gastroenterology department

Study Record Dates

• First Submitted: September 11, 2019
• First Posted: September 12, 2019
• Study Start: September 15, 2019
• Primary Completion: November 15, 2019
• Study Completion: December 15, 2019
• Last Updated: September 12, 2019

Record last verified: 2019-09