Validating and AI Software for Assessment of Children With Ear Concerns

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.

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Key information

Conditions

Otalgia Ear Diseases Earache Neurologic Manifestations Otitis Otitis Media Otitis Media Effusion Otitis Media with Effusion Otorhinolaryngologic Diseases Pain Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

6 month–6 year

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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

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

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Courtney Hill, MD

CONTACT

[email protected]

612-404-0251

Sponsors and collaborators

Lead sponsor

Glimpse Diagnostics, Inc.

Industry

Collaborators

Clinical Research Strategies
National Institute for Biomedical Imaging and Bioengineering (NIBIB)

Registry information

Official study title

Validating a Deep Learning Algorithm in Children With Ear Concerns

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: Nov 21, 2025
Registry last updated: Nov 21, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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