Validating and AI Software for Assessment of Children With Ear Concerns
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Otalgia, Otitis Media, Otitis Media Effusion. Basic parameters: 6 months — 6 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Validating a Deep Learning Algorithm in Children With Ear Concerns
Overview
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
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.
Primary outcome measures
- 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]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
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. PMID 40096786
Identifiers
NCT: NCT07243093 · Glimpse-01 · 1R44EB036883-01A1