Clinical trial NCT06969794: Single-center, Randomized, Superiority Pivotal Clinical Study to Evaluate the Efficacy of Artificial Intelligence-based Upper Gastrointestinal Endoscopy Image
Official title: Single-center, Single Group, Randomized, Superiority Pivotal Clinical Study to Evaluate the Efficacy and Safety of Artificial Intelligence-based Upper Gastrointestinal Endoscopy Image Diagnosis Aid Software
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Gastric Neoplasm
Gastric Lesion
Artificial Intelligence
Lead sponsor: Chuncheon Sacred Heart Hospital
Sponsor class: OTHER
First posted: 2025-05-14
Enrollment: 3385
Sex: ALL
Minimum age (years): 20
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A total of 3,385 endoscopic images were collected from patients who underwent gastroscopy at our institution (Chuncheon Sacred Heart Hospital from 2010 to 2017) and had adequate reference standards. Images were selected to include both neoplastic lesions and non-neoplastic findings. Inclusion criteria were images of gastric lesions that had definitive histopathology diagnoses (from endoscopic biopsy or resection) of one of five severity categories: LGD, HGD, EGC-M (mucosal early gastric carcinoma), EGC-SM (submucosal invasive early carcinoma), or AGC (advanced gastric cancer). Additionally, a set of non-neoplastic images (normal or benign findings with negative biopsies) was included as controls.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: AI performance
timeFrame: Day 1
Secondary outcomes:
measure: comparison of the AI's diagnostic performance with that of endoscopists.
timeFrame: Day 1
Other outcomes:
Locations:
city: Chuncheon
country: Korea, Republic of
facility: Chuncheon Sacred Heart hospital
state: Gangwon
Study officials:
affiliation: HALLYM UNIVERSITY COLLEGE OF MEDICINE, Korea
name: Chang Seok Bang, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: