Ignite Creation Date:
2025-12-25 @ 1:47 AM
Ignite Modification Date:
2025-12-26 @ 4:52 AM
Study NCT ID:
NCT06969794
Status:
COMPLETED
Last Update Posted:
2025-05-14
First Post:
2025-04-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Single-center, Randomized, Superiority Pivotal Clinical Study to Evaluate the Efficacy of Artificial Intelligence-based Upper Gastrointestinal Endoscopy Image
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D013274', 'term': 'Stomach Neoplasms'}], 'ancestors': [{'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D013272', 'term': 'Stomach Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 3385}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2023-08-17', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-05-10', 'studyFirstSubmitDate': '2025-04-25', 'studyFirstSubmitQcDate': '2025-05-10', 'lastUpdatePostDateStruct': {'date': '2025-05-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-05-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-08-01', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AI performance', 'timeFrame': 'Day 1', 'description': '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.'}], 'secondaryOutcomes': [{'measure': "comparison of the AI's diagnostic performance with that of endoscopists.", 'timeFrame': 'Day 1', 'description': "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.)"}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Gastric Neoplasm', 'Gastric Lesion', 'Artificial Intelligence']}, 'descriptionModule': {'briefSummary': "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.", 'detailedDescription': "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.\n\nInclusion criteria:\n\nAge 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\n\nExclusion criteria:\n\nPoor-quality images (e.g., out of focus or obscured) Lack of histopathological confirmation of the lesion\n\nEach image will be paired with a reference standard diagnosis based on the pathology result for that lesion or region."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '20 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age 19 or older\n* At least one gastric lesion biopsied with a definitive pathological diagnosis\n* Availability of high-quality white-light endoscopy images of the lesion and surrounding mucosa\n\nExclusion Criteria:\n\n* Poor-quality images (e.g., out of focus or obscured)\n* Lack of histopathological confirmation of the lesion'}, 'identificationModule': {'nctId': 'NCT06969794', 'briefTitle': 'Single-center, Randomized, Superiority Pivotal Clinical Study to Evaluate the Efficacy of Artificial Intelligence-based Upper Gastrointestinal Endoscopy Image', 'organization': {'class': 'OTHER', 'fullName': 'Chuncheon Sacred Heart Hospital'}, 'officialTitle': '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', 'orgStudyIdInfo': {'id': 'AI RCT2'}}, 'contactsLocationsModule': {'locations': [{'zip': '24253', 'city': 'Chuncheon', 'state': 'Gangwon-do', 'country': 'South Korea', 'facility': 'Chuncheon Sacred Heart hospital', 'geoPoint': {'lat': 37.87472, 'lon': 127.73417}}], 'overallOfficials': [{'name': 'Chang Seok Bang, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'HALLYM UNIVERSITY COLLEGE OF MEDICINE, Korea'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chuncheon Sacred Heart Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'PI', 'investigatorFullName': 'Chang Seok Bang', 'investigatorAffiliation': 'Chuncheon Sacred Heart Hospital'}}}}