A new smartphone app can accurately diagnose ear infections using artificial intelligence. PHOTO BY BURST/PEXELS 
A new smartphone app can accurately diagnose ear infections using artificial intelligence. PHOTO BY BURST/PEXELS 

A new smartphone app can accurately diagnose ear infections using artificial intelligence. PHOTO BY BURST/PEXELS 



By Imogen Howse

A new smartphone app can accurately diagnose ear infections using artificial intelligence.

It is hoped that the tool, developed by physician-scientists at the University of Pittsburgh, could decrease unnecessary antibiotic use in children.

Around 70 percent of children develop an ear infection before the age of one, the most common of which is acute otitis media (AOM).

However, AOM is often confused with other issues – such as fluid behind the ear – which can lead to infections being incorrectly diagnosed and incorrectly treated.

It is hoped that the tool, developed by physician-scientists at the University of Pittsburgh, could decrease unnecessary antibiotic use in children. PHOTO BY KIMIA ZARIFI/UNSPLASH 

The study’s senior author Dr. Alejandro Hoberman, a professor of pediatrics, explained: “Acute otitis media is difficult to discern from other ear conditions and so is often misdiagnosed.

“Underdiagnosis results in inadequate care and overdiagnosis results in unnecessary antibiotic treatment, which can compromise the effectiveness of currently available antibiotics.

“Our tool helps get the correct diagnosis and guide the correct treatment.”

To develop the new AI tool, Prof Hoberman and his research team built and annotated a training library of 1,151 videos of the tympanic membrane, also known as the ear drum, from 635 children who visited outpatient pediatric offices at the University of Pittsburgh’s Medical Centre between 2018 and 2023.

“The ear drum, or tympanic membrane, is a thin, flat piece of tissue that stretches across the ear canal,” explained Prof Hoberman.

“In AOM, the ear drum bulges like a bagel, leaving a central area of depression that resembles a bagel hole.

“In contrast, in children with otitis media with effusion, no bulging of the tympanic membrane is present.”

Two trained experts with extensive experience in AOM research reviewed the videos and made a diagnosis of AOM or not AOM, which the research team then used to teach two different AI models how to detect AOM.

The completed AI tool works by looking at a video of a patient’s ear drum and assessing its shape, position, color, and translucency to make a diagnosis.

Results, published in the journal JAMA Pediatrics, revealed that diagnosis was 93 percent accurate, with low rates of false negatives and false positives.

Previous studies of clinicians have revealed a diagnostic accuracy of AOM ranging from 30 to 84 percent, depending on the type of healthcare provider, level of training, and age of child being examined.

Prof Hoberman said: “These findings suggest that our tool is more accurate than many clinicians.

“It could be a gamechanger in primary health care settings to support clinicians in stringently diagnosing AOM and guiding treatment decisions.”

He added that another benefit of the tool – which makes a diagnosis by assessing a short video of the ear drum captured by an otoscope connected to a mobile phone camera – is that the videos can be stored and used to further improve diagnosis.

It is hoped that the tool, developed by physician-scientists at the University of Pittsburgh, could decrease unnecessary antibiotic use in children. PHOTO BY KIMIA ZARIFI/UNSPLASH 

“The videos we capture can be stored in a patient’s medical record and shared with other providers, meaning we can show parents and/or students what we see and explain why we are or are not making a diagnosis of ear infection,” Prof Hoberman said.

“It is important both as a teaching tool and for reassuring parents that their child is receiving appropriate treatment.”

Prof Hoberman hopes that the newly developed technology could soon be implemented widely across health care centers to enhance accurate diagnosis of AOM and support treatment decisions.

Produced in association with SWNS Talker