Validating and AI Software for Assessment of Children With Ear Concerns

NCT07243093 | Not Yet Recruiting

• 2 other identifiers: Glimpse-011R44EB036883-01A1
• Study Type: observational
• Target: 658
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Otalgia; Otitis Media; Otitis Media Effusion

Outcome Measures

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.

  Within 24 hrs of presenting to PCP or urgent care office

Eligibility Criteria

• Age: 6 Months - 6 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Children aged 6 months to 6 years presenting with ear concerns

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Glimpse Diagnostics, Inc.: lead
• National Institute for Biomedical Imaging and Bioengineering (NIBIB): collaborator
• Clinical Research Strategies: collaborator

Related Publications (1)

• 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.

  PMID: 40096786 BACKGROUND

MeSH Terms

Conditions

Earache; Otitis Media; Otitis Media with Effusion

Condition Hierarchy (Ancestors)

Ear Diseases; Otorhinolaryngologic Diseases; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Otitis

Central Study Contacts

Courtney Hill, MD

CONTACT

612-404-0251; courtney@glimpsediagnostics.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 17, 2025
• First Posted: November 21, 2025
• Study Start: January 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): July 1, 2027
• Last Updated: November 21, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share