Validating and AI Software for Assessment of Children With Ear Concerns

NCT ID: NCT07243093

Last Updated: 2025-11-21

Study Results

Results pending

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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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

658 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-01-31

Study Completion Date

2027-07-31

Brief Summary

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The goal of this observational study is to determine if the Glimpse machine learning algorithm can accurately assess ear diseases in children. Participants will:

* 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.

Detailed Description

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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.

Conditions

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Otalgia Otitis Media Otitis Media Effusion

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* Males and females aged 6 months to 6 years
* 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

* History of craniofacial abnormality
* PE tubes currently in place
* Current otorrhea
* Caretaker not having use of both hands and arms
Minimum Eligible Age

6 Months

Maximum Eligible Age

6 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Institute for Biomedical Imaging and Bioengineering (NIBIB)

NIH

Sponsor Role collaborator

Clinical Research Strategies

UNKNOWN

Sponsor Role collaborator

Glimpse Diagnostics, Inc.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Central Contacts

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Courtney Hill, MD

Role: CONTACT

612-404-0251

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.

Reference Type BACKGROUND
PMID: 40096786 (View on PubMed)

Other Identifiers

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1R44EB036883-01A1

Identifier Type: NIH

Identifier Source: secondary_id

View Link

Glimpse-01

Identifier Type: -

Identifier Source: org_study_id

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