Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

NCT05459610 | Unknown

• 1 other identifier: 2022SDU-QILU-109
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). With technical advance of image-enhanced endoscopy (IEE), studies have demonstrated IEE has high accuracy for diagnosis of GIM. The endoscopic grading system (EGGIM), a new endoscopic risk scoring system for GC, have been shown to accurately identify a wide range of patients with GIM. However, the high diagnostic accuracy of GIM using IEE and EGGIM assessments performed all require much experience, which limits the application of EGGIM. The investigators aim to design a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores.

Conditions

Intestinal Metaplasia of Gastric Mucosa; Artificial Intelligence; Endoscopy

Outcome Measures

Primary Outcomes (3)

• The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

  The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

  2 years

• The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

  The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

  2 years

• The sensitivity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture

  The sensitivity of AI model to assess the degree of intestinal metaplasia in an

  2 years

Secondary Outcomes (4)

• Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia

  2 years

• Accuracy of the inexperienced endoscopists to assess the degree of intestinal metaplasia

  2 years

• Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia

  2 years

• Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia

  2 years

Study Arms (2)

group for training the algorithm

This group of images is used for training the algorithm of the artificial intelligence

group for testing the algorithm

This group of images is used for testing the algorithm of the artificial intelligence

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Consecutive patients who receive the IEE examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study

You may qualify if:

• patients aged 18-80 years who undergo the IEE examination

You may not qualify if:

• patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
• patients with previous surgical procedures on the stomach
• patients who refuse to sign the informed consent form

Sponsors & Collaborators

• Shandong University: lead

Study Sites (1)

Department of Gastrology, QiLu Hospital, Shandong University

Jinan, Shandong, 250012, China

RECRUITING

Study Officials

• yanqing Li, MD, PHD

  Qilu Hospital of Shandong University

  STUDY CHAIR

Central Study Contacts

yanqing Li, MD, PHD

CONTACT

0531182169385; liyanqing@sdu.edu.cn

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice President of Qilu Hospital

Study Record Dates

• First Submitted: July 8, 2022
• First Posted: July 15, 2022
• Study Start: July 1, 2022
• Primary Completion: December 30, 2023
• Study Completion: December 30, 2023
• Last Updated: July 15, 2022

Record last verified: 2022-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)