Validating and AI Software for Assessment of Children With Ear Concerns
NCT ID: NCT07243093
Last Updated: 2025-11-21
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
658 participants
OBSERVATIONAL
2026-01-31
2027-07-31
Brief Summary
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* Have a video of their ear taken by their parent or their guardian
* Have a video of their ear taken by a Primary Care Physician (PCP)
* Have an assessment of their eardrums and a video of their ears taken by an Ear, Nose, and Throat specialist (ENT).
The videos will be used to determine if the Glimpse algorithm matches the diagnosis of the physicians.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* 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.
Exclusion Criteria
* PE tubes currently in place
* Current otorrhea
* Caretaker not having use of both hands and arms
6 Months
6 Years
ALL
No
Sponsors
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National Institute for Biomedical Imaging and Bioengineering (NIBIB)
NIH
Clinical Research Strategies
UNKNOWN
Glimpse Diagnostics, Inc.
INDUSTRY
Responsible Party
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Central Contacts
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References
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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.
Other Identifiers
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Glimpse-01
Identifier Type: -
Identifier Source: org_study_id
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