Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

NCT ID: NCT05459610

Last Updated: 2022-07-15

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 600 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-07-01
• Study Completion Date: 2023-12-30

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.

Detailed Description

Globally, gastric cancer is the fifth most prevalent malignancy and the third leading cause of cancer mortality. Gastric intestinal metaplasia (GIM) is an intermediate precancerous gastric lesion in the gastric cancer cascade. Studies have shown that the 5-year cumulative incidence of gastric cancer in IM patients ranges from 5.3% to 9.8% . 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

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

group for training the algorithm

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

No interventions assigned to this group

group for testing the algorithm

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

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shandong University

OTHER

Sponsor Role: lead

Responsible Party

Yanqing Li

Vice President of Qilu Hospital

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

yanqing Li, MD, PHD

Role: STUDY_CHAIR

Qilu Hospital, Shandong University

Locations

Department of Gastrology, QiLu Hospital, Shandong University

Jinan, Shandong, China

Site Status: RECRUITING

Countries

China

Central Contacts

yanqing Li, MD, PHD

Role: CONTACT

liyanqing@sdu.edu.cn

0531182169385

Facility Contacts

yanqing Li, MD, PHD

Role: primary

liyanqing@sdu.edu.cn

0531182169385

Other Identifiers

2022SDU-QILU-109

Identifier Type: -

Identifier Source: org_study_id