Artificial Intelligence Diagnostic Decision Support to Reduce Antimicrobial Prescriptions in Young Children With Colds
IMAGE
Intelligent Medical Assessment for Guiding Ear Infection Treatment
2 other identifiers
interventional
300
1 country
2
Brief 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. 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Dec 2025
2 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
February 26, 2025
CompletedFirst Posted
Study publicly available on registry
March 14, 2025
CompletedStudy Start
First participant enrolled
December 4, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 30, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
July 1, 2027
January 7, 2026
September 1, 2025
1.1 years
February 26, 2025
January 6, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Antimicrobial prescription rate
The rate of antimicrobial prescriptions will be compared between standard care and AI app groups.
Day 1
Secondary Outcomes (4)
Acute otitis media diagnosis rate
Day 1
Uninterpretable image rate
Day 1
Acute Otitis Media Severity of Symptoms (AOM-SOS) Scale version 6
From enrollment to 11 days after enrollment
Acute otitis media recurrences
From enrollment to 3 months from enrollment
Study Arms (1)
AI App + Standard of care clinical exam
EXPERIMENTALUsing 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.
Interventions
The clinician will examine the child's ear with a standard otoscope and give a clinical diagnosis and decision to treat with antibiotics.
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
Eligibility Criteria
You may qualify if:
- Age 6-24 months
- Presence of upper respiratory infection
You may not qualify if:
- No upper respiratory infection
- Otorrhea
- Tympanostomy tubes
- Currently taking antimicrobials
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Timothy Shopelead
- Merck Sharp & Dohme LLCcollaborator
Study Sites (2)
Children's Community Pediatrics Brentwood
Pittsburgh, Pennsylvania, 15227, United States
Children's Community Pediatrics Castle Shannon
Pittsburgh, Pennsylvania, 15234, United States
Related Publications (4)
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.
PMID: 38961165BACKGROUNDBedard 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.
PMID: 28101416BACKGROUNDShaikh 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.
PMID: 38436941BACKGROUNDKuruvilla 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.
PMID: 23997759BACKGROUND
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Timothy R Shope, MD, MPH
UPMC Children's Hospital
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- NA
- Masking
- NONE
- Masking Details
- The parents and the clinicians will be masked as to the AOM diagnosis by the AI app and the parents will be masked as to the clinician diagnosis until after the clinician reviews the AI app video and diagnosis.
- Purpose
- DIAGNOSTIC
- Intervention Model
- SINGLE GROUP
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Professor of Pediatrics
Study Record Dates
First Submitted
February 26, 2025
First Posted
March 14, 2025
Study Start
December 4, 2025
Primary Completion (Estimated)
December 30, 2026
Study Completion (Estimated)
July 1, 2027
Last Updated
January 7, 2026
Record last verified: 2025-09
Data Sharing
- IPD Sharing
- Will not share