OtalgiaNCT07243093
Validating and AI Software for Assessment of Children With Ear Concerns
An observational study of Otalgia and Otitis Media, run by Glimpse Diagnostics, Inc..
Trial 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… Read more about this study
- Who is it for?
- Ages 6 months to 6 years
- Conditions the study is for
- Otalgia, Otitis Media, Otitis Media Effusion
- How many people
- 658 participants
- Where
- No sites listed yet
- Study dates
- Jan 2026to Jul 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 6 months to 6 years
- All sexes
- No healthy volunteers
- Child
Study population: Children aged 6 months to 6 years presenting with ear concerns
Who may be able to join
- 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.
Who cannot join
- History of craniofacial abnormality
- PE tubes currently in place
- Current otorrhea
- Caretaker not having use of both hands and arms
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read the 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.
- Conditions
- Otalgia, Otitis Media, Otitis Media Effusion
- Medical subject headings
- Earache, Otitis Media, Otitis Media with Effusion
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Within 24 hrs of presenting to PCP or urgent care office
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Nov 2025
- Study startJan 2026 (estimated)
- Primary completionJun 2027 (estimated)
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for OtalgiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 658 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Glimpse Diagnostics, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- National Institute for Biomedical Imaging and Bioengineering (NIBIB), Clinical Research Strategies
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07243093
- Sponsor's protocol ID
- Glimpse-01
- Other IDs
- 1R44EB036883-01A1
- Status last verified
- Nov 2025
- First submitted
- 17 Nov 2025
- Last updated
- 21 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 40096786
Study at a glance
From the registry record, last updated by the sponsor on 21 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validating a Deep Learning Algorithm in Children With Ear Concerns
- ClinicalTrials.gov ID
- NCT07243093
- Acronym / sponsor ID
- Glimpse-01
- Conditions
- Otalgia, Otitis Media, Otitis Media Effusion
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 658 participants (estimated)
- Lead sponsor
- Glimpse Diagnostics, Inc.Industry (pharma, biotech, devices)
- Collaborators
- National Institute for Biomedical Imaging and Bioengineering (NIBIB), Clinical Research Strategies
- Study start
- Jan 2026 (estimated)
- Primary completion
- Jun 2027 (estimated)
- Study completion
- Jul 2027 (estimated)
- First posted
- 21 Nov 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 6 months to 6 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- Children aged 6 months to 6 years presenting with ear concerns
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (4 listed)
- History of craniofacial abnormality
- PE tubes currently in place
- Current otorrhea
- Caretaker not having use of both hands and arms
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Nov 2025.
- Background: PubMed 40096786Bryton 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Otalgia and Otitis Media, run by Glimpse Diagnostics, Inc..
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 21 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Courtney Hill, MD (612-404-0251, courtney@glimpsediagnostics.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07243093, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Nov 2025.