Gastric NeoplasmNCT06969794
Single-center, Randomized, Superiority Pivotal Clinical Study to Evaluate the Efficacy of Artificial Intelligence-based Upper Gastrointestinal Endoscopy Image
An observational study of Gastric Neoplasm and Gastric Lesion, run by Chuncheon Sacred Heart Hospital at 1 site in South Korea.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 to 100 years
- Conditions the study is for
- Gastric Neoplasm, Gastric Lesion, Artificial Intelligence
- How many people
- 3,385 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Jul 2023to 17 Aug 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- Poor-quality images (e.g., out of focus or obscured)
- Lack of histopathological confirmation of the lesion
Trial locations and contacts
1 site in 1 country.
South Korea1 site
Chuncheon Sacred Heart hospital
Chuncheon, Gangwon-do, 24253
About this study
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
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.
Read the 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.
- Conditions
- Gastric Neoplasm, Gastric Lesion, Artificial Intelligence
- Medical subject headings
- Stomach Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
AI performance
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.
Measured: Day 1
Secondary outcomes (1)
comparison of the AI's diagnostic performance with that of endoscopists.
Secondary outcomes included comparison of the AI's diagnostic performance with that of endoscopists. (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.)
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 May 2025
- Study start1 Jul 2023
- Primary completion1 Aug 2023
- Study completion17 Aug 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric NeoplasmStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 3,385 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chuncheon Sacred Heart Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chang Seok Bang, Chuncheon Sacred Heart Hospital
- Investigators
- Chang Seok Bang, MD, PhD, Principal investigator, HALLYM UNIVERSITY COLLEGE OF MEDICINE, Korea
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06969794
- Sponsor's protocol ID
- AI RCT2
- Status last verified
- May 2025
- First submitted
- 25 Apr 2025
- Last updated
- 14 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06969794
- Acronym / sponsor ID
- AI RCT2
- Conditions
- Gastric Neoplasm, Gastric Lesion, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 3,385 participants (actual)
- Lead sponsor
- Chuncheon Sacred Heart HospitalUniversity, hospital or other
- Study start
- 1 Jul 2023 (actual)
- Primary completion
- 1 Aug 2023 (actual)
- Study completion
- 17 Aug 2023 (actual)
- First posted
- 14 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 20 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- Poor-quality images (e.g., out of focus or obscured)
- Lack of histopathological confirmation of the lesion
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
AI performance
Time frame: Day 1
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.
1 secondary outcome
comparison of the AI's diagnostic performance with that of endoscopists.
Time frame: Day 1
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 May 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Chang Seok Bang, MD, PhD principal investigator
HALLYM UNIVERSITY COLLEGE OF MEDICINE, Korea
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Neoplasm and Gastric Lesion, run by Chuncheon Sacred Heart Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 14 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI performance (time frame: Day 1). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 May 2025; AzTrials last refreshed its copy on 5 Nov 2026.