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Parent-led Assessment and Recognition of AOM Using Digital Ear Imaging
Acute otitis media (AOM) is the most common bacterial infection in young children and a major contributor to healthcare utilization and costs. Diagnosis relies on otoscopy and identification of tympanic membrane (TM) bulging, yet physician-performed otoscopy has variable diagnostic accuracy and depends heavily on clinical expertise. Digital otoscopy combined with artificial intelligence (AI)-based image analysis has demonstrated high diagnostic accuracy in previous studies. In a recent study by our group, digital otoscopy with an automated neural network classifier reliably ruled out otitis media in children. The PARENT-EAR randomized controlled pilot trial aims to evaluate whether parent-performed digital otoscopy at home, combined with retrospective AI analysis of captured TM images, can reduce physician visits due to suspicion of AOM. The study also assesses feasibility, diagnostic accuracy of parents after training, and potential effects on antibiotic use and healthcare utilization. Methods and outcomes The PARENT-EAR trial is a randomized controlled pilot trial conducted at Vaasa Central Hospital and Turku University Hospital. Recruitment will occur between September and December 2026 (extended if necessary), with follow-up ending May 31, 2027 (5-9 months per participant). Children aged 12 to 47 months with at least one AOM episode in the past 12 months will be randomized 1:1 using permuted block randomization. Exclusion criteria include tympanostomy tubes, tympanic membrane perforation, middle ear effusion at recruitment, immunosuppression, previously diagnosed hearing loss or anatomical abnormalities predisposing to AOM, and otoscopy experience of the parent. Intervention group: Parents receive a digital otoscope (The Ear Puffin by Sibbo Medical Devices, MDR EU Class I device) and training on TM image interpretation. They complete a TM image classification quiz before and after the training. During follow-up, parents perform home otoscopy when suspectin
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