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Acute Otitis Media (AOM)IMAGENCT06876259

Artificial Intelligence Diagnostic Decision Support to Reduce Antimicrobial Prescriptions in Young Children With Colds

A study testing Standard Clinical Ear Exam and AI app ear exam and diagnosis in people with Acute Otitis Media (AOM) and Upper Respiratory Infection, run by Timothy Shope at 2 sites in United States.

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Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record

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Trial summary

Ear infections are common in young children with cold symptoms, but they can be difficult to diagnose due to small ear canals, child movement, and limited viewing time. In this study, investigators will take photos of the eardrums of children 6-24 months of age with upper respiratory symptoms. Read more about this study

Who is it for?
Ages 6 to 24 months
What is being tested?
Standard Clinical Ear Exam and AI app ear exam and diagnosis
Could I receive a placebo?
No, everyone receives an active treatment
How many people
300 participants
Where
2 sitesUnited States
Study dates
4 Dec 2025to 1 Jul 2027
Phase
Interventional

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 to 24 months
  • All sexes
  • No healthy volunteers
  • Child

Who may be able to join

  • Age 6-24 months
  • Presence of upper respiratory infection

Who cannot join

  • No upper respiratory infection
  • Otorrhea
  • Tympanostomy tubes
  • Currently taking antimicrobials

Trial locations and contacts

2 sites in 1 country. Contact the study team or a site near you to ask about taking part.

Timothy R Shope, MD, MPH contact

412-692-5471

timothy.shope@chp.edu

Nader Shaikh, MD, MPH contact

412-996-2653

nader.shaikh@chp.edu

United States2 sites, 2 recruiting
  • Children's Community Pediatrics Brentwood

    Pittsburgh, Pennsylvania, 15227

    Patrick Tate, MD, 412-443-7980, patrick.tate1@chp.edu

    Recruiting
  • Children's Community Pediatrics Castle Shannon

    Pittsburgh, Pennsylvania, 15234

    Marc Yester, MD, 336-251-8024, yesterma@upmc.edu

    Recruiting

Other trials in United States

About this study

Official title: Intelligent Medical Assessment for Guiding Ear Infection Treatment

Ear infections are common in young children with cold symptoms, but they can be difficult to diagnose due to small ear canals, child movement, and limited viewing time. In this study, investigators will take photos of the eardrums of children 6-24 months of age with upper respiratory symptoms. The photos will be reviewed by imaging software enhanced with artificial intelligence (AI app) to determine whether the AI app changes how ear infections are diagnosed and treated. The AI app has undergone rigorous study and was found to be highly accurate; but how using this technology affects the diagnosis and treatment by clinicians has not been studied. This research may help improve diagnostic accuracy for ear infections and ensure antibiotics are prescribed only for those children who have definite ear infections.

Read the detailed description

Participants and Setting This will be a 12-month, within-subject design, single center study of 300 children 6 to 24 months of age presenting to their primary care providers at Children's Community Pediatrics offices with upper respiratory symptoms. Exclusion criteria are children with tympanostomy tubes or purulent otorrhea, absence of upper respiratory symptoms, or who are currently taking antimicrobials.

Design and Outcomes

Using a double blind, within-subject design, each child's ear will be assessed by the AI app and a standard clinical exam. The primary outcome measure will be antimicrobial prescription rates derived from 150 paired images that each have an AI app and clinician diagnosis. We will secondarily assess acute otitis media (AOM) diagnosis rates. If the AI app diagnoses AOM it will always prescribe an antimicrobial. Secondary outcomes are described below:

  • Implementation challenges reflected by the proportion of uninterpretable images (cerumen, uncooperative patient, poor technique).
  • Symptom resolution and side effects of antimicrobial use for 10 days after enrollment, regardless of whether children are diagnosed with AOM or prescribed antimicrobials. We will monitor symptoms of AOM daily using a validated symptom scale entered every evening by parents in electronic diaries which we have used in many other studies. If any participant increases their symptom score by >20% at any time, we will contact them and offer a visit. Rates of protocol-defined diarrhea and diaper rash which are the most common side effects of antimicrobial use in this age group will be assessed.
  • AOM recurrences by reviewing the medical record for 3 months following enrollment.

Sample Size Calculation Using paired observations (AI app vs clinician antimicrobial prescription recommendations), an estimated 300 children 6 to 24 months of age presenting to primary care practices with upper respiratory symptoms will need to be enrolled to derive 150 paired interpretable images to detect a 10% difference in AOM diagnosis and subsequent antimicrobial prescription rates between the AI app and clinicians, assuming a power of 80% and two-sided p-value <0.05. This assumes 15% of children in the target population will truly have AOM, and clinicians will diagnose and treat AOM at a 10% higher rate (25%) compared to the AI app. If clinicians reconcile and follow the app diagnosis and treatment recommendations, this will equate to a 40% reduced/avoidable antimicrobial prescription rate. This estimate accounts for two ears per child and estimates 50% of images will be uninterpretable by the app, and 50% of clinician exams will not result in a diagnosis.

Statistical analysis All analyses will be conducted by a statistician in the General Academic Pediatrics Division, Department of Pediatrics.

The primary outcome of antimicrobial prescription rates will be assessed by the McNemar's Test. Differences in secondary outcomes will be assessed by generalized estimating equation (symptom score) and chi square test (antimicrobial side effects and recurrent AOM).

Descriptive outcomes (uninterpretable images, clinician ability to make a diagnosis, AOM diagnosis, antimicrobial prescriptions) will be reported as rates (%).

Study Procedures:

Screening: Office schedules will be screened to identify children in the eligible age group who are presenting with upper respiratory symptoms.

Consent: Once age-eligible children have checked in to the office their parents will be approached by research personnel to assess their interest in the study and eligibility. Informed consent will be obtained after rooming.

Study Procedures:

The duration of the study procedures, not including standard clinical care, will be about 10 minutes. After consent, each participating child will have two ear exams.

  1. The first exam will be done before the clinician enters the examination room, by study personnel using the AI app (research intervention). The device is a standard otoscope head attached to an iPhone. Only the ear speculum touches the child. The child may be held by the parent or staff either prone or upright. There will be two attempts per ear and attempts will stop at parent request. Study staff may need to remove cerumen (ear wax) using gentle irrigation or curette. Study staff are clinicians experienced in and qualified to remove wax. We expect a significant proportion of unusable images and have accounted for this in our sample size calculation and study flow chart. If there is an uninterpretable image by the app (cerumen, poor image, or poor cooperation) in both ears, the participant will exit the study, and the clinician will be informed before entering the room so that normal care can ensue (cerumen removal for example). If an interpretable image is captured in at least one ear, the diagnosis ("app diagnosis") will be recorded in the data form and blinded to the parents and the clinician. (The app only records a video initially, then the user selects a brief section of the highest quality video to render the diagnosis, which can be done outside of the room).
  2. Study personnel will then step out of the room and the clinician will enter and do the second exam (standard care). (Clinicians may remove cerumen also by irrigation or curette, if necessary, even if the app obtained an interpretable view. Sometimes cerumen can fall into the canal with entry or exit of an exam speculum.) Parents again will be blinded to the clinician's findings - there will be no discussion about the diagnosis or treatment.
  3. The clinician will then step out of the examination room and document a diagnosis ("clinician diagnosis") and treatment decision without consulting the app ("app diagnosis").
  4. The clinician will then view the app video and diagnosis, and then record their "reconciliation diagnosis" and treatment decision.
  5. Clinicians will then reenter the exam room and inform the parents of their diagnosis and treatment.
  6. Clinicians will complete a "final outcome" indicating whether antimicrobials were prescribed and the reason for prescription. Data will be recorded in an electronic database.
  7. If participants have a paired image (app and clinician rendered a diagnosis on the same ear) they will complete an AOM symptom score scale (duration: 60 seconds) and instructed how to enter the same symptom score in an electronic diary (duration: two minutes).

Demographic Information:

Research staff will obtain demographic information after the study procedures (duration: 60 seconds).

Follow-up:

All participants will be followed for 10 days to assess symptom resolution and side effects of antimicrobial use. AOM symptoms will be monitored daily for 10 days using a validated symptom scale entered once every evening (duration: 60 seconds), whether on antimicrobial therapy or not, by parents in electronic diaries which have used by our team in many other studies. If any participant increases their symptoms score by >20% at any time, they will be contacted and offered a visit. Rates of protocol-defined diarrhea and diaper rash which are the most common side effects of antimicrobial use in this age group will also be assessed. Finally, AOM recurrences will be monitored by reviewing the electronic medical record for 3 months following enrollment.

Duration:

  1. The app will only be used once, before the clinician exam.
  2. The ear symptom score will be done at the enrollment visit and then for the next 10-11 days in electronic diaries.
  3. Text message reminders to complete the diaries will go for 10-11 days after the enrollment visit.
  4. Medical record review will proceed for 90 days after enrollment.
Being tested
Standard Clinical Ear Exam, AI app ear exam and diagnosis
Keywords
acute otitis media, diagnosis, artificial intelligence, diagnostic classifier, antimicrobial stewardship
Medical subject headings
Otitis Media, Respiratory Tract Infections, Disease

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Standard Clinical Ear ExamDiagnostic test

    The clinician will examine the child's ear with a standard otoscope and give a clinical diagnosis and decision to treat with antibiotics.

  • AI app ear exam and diagnosisDiagnostic test

    Using a standard otoscope with a cell phone mounted to it, research personnel will record a video image of the tympanic membrane and send it to the cloud for analysis using AI enhanced classification software to render a diagnosis and treatment recommendation

Study groups in NCT06876259
Study groupRoleWhat they receive
AI App + Standard of care clinical examExperimentalUsing a within subject design, each child's ear will be in the experimental and standard care group. Each ear will be examined by the AI app and a clinician (blinded to the AI app diagnosis) to provide a diagnosis and treatment recommendation.
Receives: Diagnostic Test: Standard Clinical Ear Exam; Diagnostic Test: AI app ear exam and diagnosis

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Antimicrobial prescription rate

    The rate of antimicrobial prescriptions will be compared between standard care and AI app groups.

    Measured: Day 1

Secondary outcomes (4)
  1. Acute otitis media diagnosis rate

    The rate of diagnosis of acute otitis media will be compared between standard clinical examination and the AI app

    Measured: Day 1

  2. Uninterpretable image rate

    The rate of AI app and clinician interpretable images among enrolled participants

    Measured: Day 1

  3. Acute Otitis Media Severity of Symptoms (AOM-SOS) Scale version 6

    A validated and reliable 10-item scale for measuring AOM symptom burden. Responses for each item are scored 0-5 and the total score minimum is 0 and maximum is 50. Higher values represent more severe symptoms.

    Measured: From enrollment to 11 days after enrollment

  4. Acute otitis media recurrences

    All participants with paired clinician and AI app images in the same ear will be followed for 3 months to assess recurrences of AOM. This will be determined from diagnostic codes entered into the electronic medical record. A new AOM diagnosis must be at least 14 days after the previous diagnosis. AOM recurrences will be expressed as total counts among participants and total counts among ears.

    Measured: From enrollment to 3 months from enrollment

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted14 Mar 2025
  2. Study start4 Dec 2025
  3. Primary completion30 Mar 2027 (estimated)
  4. Study completion1 Jul 2027 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Diagnosis
Allocation
Not applicable
Intervention model
Single group
Model description
Participants in this within subjects study design will have their ears examined by the scope connected to the AI app and by a clinician. Therefore, the participants crossover or participate in both the intervention and standard care groups.
Masking
None (open label)
Enrolment
300 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06876259
Sponsor's protocol ID
STUDY24090131
Other IDs
MISP102729
Acronym
IMAGE
Status last verified
Oct 2026
First submitted
26 Feb 2025
Last updated
8 Oct 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
Yes
Individual data sharing
No

Publications

  • Shaikh N, Lee MC, Kurs-Lasky M. Modification of an outcome measure to follow symptoms of children with acute otitis media. Pediatr Res. 2025 Feb;97(2):695-699. doi: 10.1038/s41390-024-03390-2. Epub 2024 Jul 3.

    PubMed 38961165
  • Bedard N, Shope T, Hoberman A, Haralam MA, Shaikh N, Kovacevic J, Balram N, Tosic I. Light field otoscope design for 3D in vivo imaging of the middle ear. Biomed Opt Express. 2016 Dec 14;8(1):260-272. doi: 10.1364/BOE.8.000260. eCollection 2017 Jan 1.

    PubMed 28101416
  • Shaikh N, Conway SJ, Kovacevic J, Condessa F, Shope TR, Haralam MA, Campese C, Lee MC, Larsson T, Cavdar Z, Hoberman A. Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children. JAMA Pediatr. 2024 Apr 1;178(4):401-407. doi: 10.1001/jamapediatrics.2024.0011.

    PubMed 38436941
  • Kuruvilla A, Shaikh N, Hoberman A, Kovacevic J. Automated diagnosis of otitis media: vocabulary and grammar. Int J Biomed Imaging. 2013;2013:327515. doi: 10.1155/2013/327515. Epub 2013 Aug 7.

    PubMed 23997759

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