Ignite Creation Date:
2025-12-24 @ 6:47 PM
Ignite Modification Date:
2026-01-07 @ 10:46 AM
Study NCT ID:
NCT06143657
Status:
UNKNOWN
Last Update Posted:
2023-12-06
First Post:
2023-11-16
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Clinical Performance Evaluation of AI-Enabled Automated Gram Staining Device
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001424', 'term': 'Bacterial Infections'}], 'ancestors': [{'id': 'D001423', 'term': 'Bacterial Infections and Mycoses'}, {'id': 'D007239', 'term': 'Infections'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1600}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2024-01-10', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-11', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-11-30', 'studyFirstSubmitDate': '2023-11-16', 'studyFirstSubmitQcDate': '2023-11-16', 'lastUpdatePostDateStruct': {'date': '2023-12-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-11-22', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Exploratory evaluation items', 'timeFrame': 'Day 1', 'description': "The difference in accuracy rates of microscopic examination findings by technicians at each facility based on the technicians' years of experience."}], 'primaryOutcomes': [{'measure': 'Primary Outcome', 'timeFrame': 'Day 1', 'description': 'The difference in accuracy rates between the Gram staining examination performed by laboratory technicians and the automated Gram staining device, including the approximate bacterial quantity based on Gram staining and morphology (categorized as -, 1+, 2+, 3+, 4+), presence of white blood cells, presence of phagocytic images, and, in the case of sputum, the Geckler classification.'}], 'secondaryOutcomes': [{'measure': 'Secondary Outcome', 'timeFrame': 'Day 1', 'description': 'Impact of specimen type and identified bacterial species on testing accuracy.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Infection, Bacterial']}, 'descriptionModule': {'briefSummary': 'The investigators have developed an analysis AI for Gram staining. In this study, the investigators will compare the testing accuracy of automated Gram staining equipment with AI with the testing accuracy of laboratory technicians. Based on the results, the investigators will examine the possibility of clinical application of the automated Gram staining device.', 'detailedDescription': 'Gram staining,whitch provides crucial information for selecting antibiotics in the diagnosis and treatment of infectious diseases, is often manually performed in many laboratories, and the microscopic findings are subjectively interpreted and reported based on the experience of laboratory technicians. Automating the entire process from staining to microscopic examination, as achieved by the AI-equipped automatic Gram staining device, can reduce the labor required for Gram staining. This automation enables even less experienced technicians to quickly report Gram staining results.\n\nThe aim of this study is to evaluate the accuracy of the Auto microscopic examination of Gram-stained samples using anonymized processed slides created from clinical specimens. This evaluation will be compared with the accuracy of microscopic examinations performed by hospital laboratory technicians.\n\nIf the accuracy of the machine is demonstrated, it can be registered as a medical device and proceed to the implementation stage in actual clinical settings. The implementation of this device in clinical laboratories is expected to reduce workload, expedite processes, and contribute to the optimization of antibiotic use based on Gram staining results.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Specimens for microbiological testing, such as Gram staining, ordered at the hospital providing the specimens.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1\\. Patients in whom a Gram stain test was ordered\n\nExclusion Criteria:\n\nNo exclusion criteria'}, 'identificationModule': {'nctId': 'NCT06143657', 'briefTitle': 'Clinical Performance Evaluation of AI-Enabled Automated Gram Staining Device', 'organization': {'class': 'INDUSTRY', 'fullName': 'GramEye'}, 'officialTitle': 'Clinical Performance Evaluation of AI-Enabled Automated Gram Staining Device', 'orgStudyIdInfo': {'id': 'K22581'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Automated Gram Staining Machine analysis', 'type': 'DIAGNOSTIC_TEST', 'description': 'Measure urine and sputum specimens using this device.'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Yamada Tatsuya, M.D.', 'role': 'CONTACT', 'email': 'tatsuya.yamada@grameye.com', 'phone': '7028060149'}], 'overallOfficials': [{'name': 'Yoshifumi UwaminoUwamino, Ph.D.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Assistant professor, Keio University hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'GramEye', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}