Ignite Creation Date:
2025-12-25 @ 1:05 AM
Ignite Modification Date:
2025-12-26 @ 9:14 AM
Study NCT ID:
NCT07243093
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-11-21
First Post:
2025-11-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Validating and AI Software for Assessment of Children With Ear Concerns
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D004433', 'term': 'Earache'}, {'id': 'D010033', 'term': 'Otitis Media'}, {'id': 'D010034', 'term': 'Otitis Media with Effusion'}], 'ancestors': [{'id': 'D004427', 'term': 'Ear Diseases'}, {'id': 'D010038', 'term': 'Otorhinolaryngologic Diseases'}, {'id': 'D010146', 'term': 'Pain'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D010031', 'term': 'Otitis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 658}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2027-07', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-17', 'studyFirstSubmitDate': '2025-11-17', 'studyFirstSubmitQcDate': '2025-11-17', 'lastUpdatePostDateStruct': {'date': '2025-11-21', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-11-21', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2027-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': "Percent agreement of Glimpse machine learning algorithm's classification of a child's ear image with an ENT panel diagnosis", 'timeFrame': 'Within 24 hrs of presenting to PCP or urgent care office', 'description': "The primary endpoint of this study is to compare the percent agreement of Glimpse machine learning algorithm's classification of a child's ear image with an ENT panel diagnosis of the same child's ear for the diagnoses of acute otitis media (AOM), otitis media with effusion (OME), and no middle ear effusion, versus the percent agreement of primary care provider's (PCP) diagnosis with an ENT panel diagnosis, of in children with otalgia."}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isUnapprovedDevice': True, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Otalgia', 'Otitis Media', 'Otitis Media Effusion']}, 'referencesModule': {'references': [{'pmid': '40096786', 'type': 'BACKGROUND', 'citation': 'Bryton C, Surapaneni S, Rangarajan N, Hong A, Marston AP, Vecchiotti MA, Hill C, Scott AR. Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population. Int J Pediatr Otorhinolaryngol. 2025 May;192:112311. doi: 10.1016/j.ijporl.2025.112311. Epub 2025 Mar 13.'}]}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to determine if the Glimpse machine learning algorithm can accurately assess ear diseases in children. Participants will:\n\n* Have a video of their ear taken by their parent or their guardian\n* Have a video of their ear taken by a Primary Care Physician (PCP)\n* Have an assessment of their eardrums and a video of their ears taken by an Ear, Nose, and Throat specialist (ENT).\n\nThe videos will be used to determine if the Glimpse algorithm matches the diagnosis of the physicians.', 'detailedDescription': 'Ear complaints, including earache (otalgia), are the most common reasons children seek healthcare and routinely bring children into the office of a pediatrician or urgent care setting. This study will assess children who present with signs and symptoms of otitis media to the primary care office or urgent care. Participants will receive their standard of care from their treating physician, with study assessments including videos of their ears taken by their parent or guardian and the treating physician. Once this is complete, participants will see an ENT for an assessment of their eardrum. The ENT assessment will occur within 24 hours of the PCP visit and will not be used to inform patient treatment.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '6 Years', 'minimumAge': '6 Months', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Children aged 6 months to 6 years presenting with ear concerns', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Males and females aged 6 months to 6 years\n* Presenting to a pediatrician's office or urgent care with signs and symptoms of otitis media, including tugging at ears, ear pain, crying at night, refusing to lie flat, sleeping poorly, having a fever, having decreased appetite, and/or concern for hearing loss, regardless of previous diagnosis of AOM or OME.\n\nExclusion Criteria:\n\n* History of craniofacial abnormality\n* PE tubes currently in place\n* Current otorrhea\n* Caretaker not having use of both hands and arms"}, 'identificationModule': {'nctId': 'NCT07243093', 'briefTitle': 'Validating and AI Software for Assessment of Children With Ear Concerns', 'organization': {'class': 'INDUSTRY', 'fullName': 'Glimpse Diagnostics, Inc.'}, 'officialTitle': 'Validating a Deep Learning Algorithm in Children With Ear Concerns', 'orgStudyIdInfo': {'id': 'Glimpse-01'}, 'secondaryIdInfos': [{'id': '1R44EB036883-01A1', 'link': 'https://reporter.nih.gov/quickSearch/1R44EB036883-01A1', 'type': 'NIH'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Courtney Hill, MD', 'role': 'CONTACT', 'email': 'courtney@glimpsediagnostics.com', 'phone': '612-404-0251'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Glimpse Diagnostics, Inc.', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'National Institute for Biomedical Imaging and Bioengineering (NIBIB)', 'class': 'NIH'}, {'name': 'Clinical Research Strategies', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}