Validating and AI Software for Assessment of Children With Ear Concerns

Validating a Deep Learning Algorithm in 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.

Study Overview

• Status: Not yet recruiting
• Conditions: Otitis Media; Otitis Media Effusion; Otalgia

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.

• Study Type: Observational
• Enrollment (Estimated): 658

Contacts and Locations

• Study Contact: Name: Courtney Hill, MD; Phone Number: 612-404-0251; Email: courtney@glimpsediagnostics.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Children aged 6 months to 6 years presenting with ear concerns

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

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

Publications and helpful links

General 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.

Study record dates

Study Major Dates

• Study Start (Estimated): January 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): July 1, 2027

Study Registration Dates

• First Submitted: November 17, 2025
• First Submitted That Met QC Criteria: November 17, 2025
• First Posted (Actual): November 21, 2025

Study Record Updates

• Last Update Posted (Actual): November 21, 2025
• Last Update Submitted That Met QC Criteria: November 17, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Otorhinolaryngologic Diseases; Ear Diseases; Otitis; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Otitis Media; Otitis Media with Effusion; Earache
• Other Study ID Numbers: Glimpse-01; 1R44EB036883-01A1 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No