Otalgia, Otitis Media, Otitis Media Effusion
Conditions
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.
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.
Interventions
None listed
Sponsors
National Institute for Biomedical Imaging and Bioengineering (NIBIB)
Clinical Research Strategies
Glimpse Diagnostics, Inc.
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
6 Months to 6 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percent agreement of Glimpse machine learning algorithm's classification of a child's ear image with an ENT panel diagnosis | 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. |
Contacts
Primary ContactCourtney Hill, MD
Outcome results
None listed