Single-center, Randomized, Superiority Pivotal Clinical Study to Evaluate the Efficacy of Artificial Intelligence-based Upper Gastrointestinal Endoscopy Image

NCT ID: NCT06969794

Last Updated: 2025-05-14

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

3385 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-07-01

Study Completion Date

2023-08-17

Brief Summary

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We will conduct a single-center retrospective study at a university hospital. A total of 3,385 gastroscopic white-light images from patients with pathologically confirmed findings will be analyzed. The AI software will automatically identify images as non-neoplastic or neoplastic (low-grade dysplasia, high-grade dysplasia, early gastric cancer with mucosal or submucosal invasion, or advanced gastric cancer) and highlighted lesion locations. Two experienced endoscopists will independently review the same image set without AI assistance for comparison. Primary outcomes are sensitivity and specificity of the AI in detecting gastric neoplasms (by category and overall), and the localization accuracy measured by the localization receiver operating characteristic (LROC) curve area. Secondary outcomes is includes comparison of the AI's diagnostic performance with that of endoscopists.

Detailed Description

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We will conduct a single-center retrospective study at a university hospital. A total of 3,385 gastroscopic white-light images from patients with pathologically confirmed findings will be analyzed. The AI software will automatically identify images as non-neoplastic or neoplastic (low-grade dysplasia, high-grade dysplasia, early gastric cancer with mucosal or submucosal invasion, or advanced gastric cancer) and highlighted lesion locations. Two experienced endoscopists will independently review the same image set without AI assistance for comparison. Primary outcomes are sensitivity and specificity of the AI in detecting gastric neoplasms (by category and overall), and the localization accuracy measured by the localization receiver operating characteristic (LROC) curve area. Secondary outcomes is includes comparison of the AI's diagnostic performance with that of endoscopists.

Inclusion criteria:

Age 19 or older At least one gastric lesion biopsied with a definitive pathological diagnosis Availability of high-quality white-light endoscopy images of the lesion and surrounding mucosa

Exclusion criteria:

Poor-quality images (e.g., out of focus or obscured) Lack of histopathological confirmation of the lesion

Each image will be paired with a reference standard diagnosis based on the pathology result for that lesion or region.

Conditions

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Gastric Neoplasm Gastric Lesion Artificial Intelligence

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* Age 19 or older
* At least one gastric lesion biopsied with a definitive pathological diagnosis
* Availability of high-quality white-light endoscopy images of the lesion and surrounding mucosa

Exclusion Criteria

* Poor-quality images (e.g., out of focus or obscured)
* Lack of histopathological confirmation of the lesion
Minimum Eligible Age

20 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Chuncheon Sacred Heart Hospital

OTHER

Sponsor Role lead

Responsible Party

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Chang Seok Bang

PI

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Chang Seok Bang, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

HALLYM UNIVERSITY COLLEGE OF MEDICINE, Korea

Locations

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Chuncheon Sacred Heart hospital

Chuncheon, Gangwon-do, South Korea

Site Status

Countries

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South Korea

Other Identifiers

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AI RCT2

Identifier Type: -

Identifier Source: org_study_id

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