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Not yet recruitingNCT07243093Updated Nov 21, 2025

Validating and AI Software for Assessment of Children With Ear Concerns

An observational study in Otalgia, Otitis Media and Otitis Media Effusion, sponsored by Glimpse Diagnostics, Inc.. Not yet recruiting. Open to participants aged 6 Months to 6 Years. Per ClinicalTrials.gov, last updated 2025-11-21.

Sponsored by Glimpse Diagnostics, Inc. · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
658
Ages
6 Months to 6 Years
Sex
All
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Study summary

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.

Read the detailed description

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.

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Conditions studied

  • Otalgia
  • Otitis Media
  • Otitis Media Effusion
03

In context

Otitis Media

284 studies on the registry are indexed under Otitis Media; 33 are open to participants now.

This study's planned enrollment of 658 is above the median of 155 across 72 observational studies indexed under Otitis Media.

Browse Otitis Media studies →

Lead sponsor

This is the only study on the registry with Glimpse Diagnostics, Inc. as lead sponsor.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
6 Months to 6 Years
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Children aged 6 months to 6 years presenting with ear concerns

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

Exclusion Criteria:

  • History of craniofacial abnormality
  • PE tubes currently in place
  • Current otorrhea
  • Caretaker not having use of both hands and arms
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Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
658 participants (estimated)
Patient registry
No
06

What researchers measure

Primary outcomes

  1. Percent agreement of Glimpse machine learning algorithm's classification of a child's ear image with an ENT panel diagnosis

    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.

    Time frame: Within 24 hrs of presenting to PCP or urgent care office

07

Study locations

No study locations are listed for this record.

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References and documents

Publications

  • 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. PubMed 40096786 ↗

Individual participant data

Plan to share: No

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Nov 21, 2025, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT07243093
Lead sponsor
Glimpse Diagnostics, Inc.
Collaborators
National Institute for Biomedical Imaging and Bioengineering (NIBIB), Clinical Research Strategies
Responsible party
Sponsor
First posted
Nov 21, 2025
Start date
Jan 2026 (estimated)
Primary completion
Jun 2027 (estimated)
Completion
Jul 2027 (estimated)
Last update
Nov 21, 2025

Study contacts

Courtney Hill, MD
Contact
courtney@glimpsediagnostics.com
612-404-0251

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is not yet recruiting, as verified in Nov 2025. You cannot join it, but the record below documents what was studied.

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Discussion

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