Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence

Artificial Intelligence Clinical Trial

Official title:

Development and Validation of an Artificial Intelligence System for Automatic Evaluation of the Extent of Intestinal Metaplasia

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05459610
• Other study ID #: 2022SDU-QILU-109
• Status: Recruiting
• Start date: July 1, 2022
• Est. completion date: December 30, 2023

Study information

• Verified date: July 2022
• Source: Shandong University
• Contact: yanqing Li, MD, PHD
• Phone: 0531182169385
• Email: liyanqing[@]sdu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 600
• Est. completion date: December 30, 2023
• Est. primary completion date: December 30, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

• Artificial Intelligence
• Endoscopy
• Intestinal Metaplasia of Gastric Mucosa
• Metaplasia

Locations

Country	Name	City	State
China	Department of Gastrology, QiLu Hospital, Shandong University	Jinan	Shandong

Sponsors (1)

Lead Sponsor	Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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
Primary	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	Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia	Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia in an endoscopic picture	2 years
Secondary	Accuracy of the inexperienced endoscopists to assess the degree of intestinal metaplasia	Accuracy of the inexperienced endoscopists to assess the degree of intestinal metaplasia in an endoscopic picture	2 years
Secondary	Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia	Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia in an endoscopic picture	2 years
Secondary	Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia	Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia in an endoscopic picture	2 years