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
Status
Conditions
Conditions
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Courtney Hill, MD
- Phone Number: 612-404-0251
- Email: courtney@glimpsediagnostics.com
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
Accepts Healthy Volunteers
Sampling Method
Study Population
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?
Design Details
What is the study measuring?
Primary Outcome Measures
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
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.
|
Within 24 hrs of presenting to PCP or urgent care office
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
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)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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