Acute otitis media (AOM) is one of the most common childhood infections, but the accuracy of its diagnosis has been consistently low. With the help of AI-powered tools, physicians could potentially improve their diagnostic capabilities and better distinguish AOM from other ear conditions. While several neural networks have been developed for recognizing AOM, their clinical application has been limited thus far.
Now, physician-scientists at UPMC and the University of Pittsburgh have developed a new smartphone app that uses artificial intelligence (AI) to accurately diagnose ear infections or acute otitis media (AOM). This could help reduce the unnecessary use of antibiotics in young children.
The new AI tool makes a diagnosis by assessing a short video of the eardrum captured by an otoscope connected to a smartphone camera and offers a simple and effective solution that could be more accurate than trained clinicians.
“Acute otitis media is often incorrectly diagnosed,” said senior author Alejandro Hoberman, M.D., professor of pediatrics and director of the Division of General Academic Pediatrics at Pitt’s School of Medicine and president of UPMC Children’s Community Pediatrics. “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 right treatment.”
According to Hoberman, almost 70% of children experience an ear infection before their first birthday. However, accurately diagnosing acute otitis media (AOM) can be tricky and requires a trained eye to detect subtle visual findings of the eardrum in a wriggly baby. Unfortunately, AOM is often confused with otitis media with effusion, a condition that does not involve bacteria and does not require antimicrobial treatment.
To address this issue, Hoberman and his team have developed a practical tool to improve the accuracy of AOM diagnosis. They started by building a training library of 1,151 videos of the tympanic membrane from 635 children who visited pediatric offices between 2018 and 2023. Two experts with extensive experience in AOM research reviewed the videos and made a diagnosis of AOM or not AOM.
“The eardrum, or tympanic membrane, is a thin, flat piece of tissue that stretches across the ear canal,” said Hoberman. “In AOM, the eardrum 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.”
In the recent study, the researchers utilized a training library of 921 videos to teach the models to teach two different AI models to detect AOM by looking at features of the tympanic membrane, including shape, position, color, and translucency.
They then used 230 videos to test the models’ performance and found that both models had greater than 93% sensitivity and specificity, indicating low rates of false positives and false negatives. In comparison, previous studies have reported diagnostic accuracy of AOM ranging from 30% to 84% among clinicians, depending on factors such as their level of training, type of healthcare provider, and children’s age.
“These findings suggest that our tool is more accurate than many clinicians,” said Hoberman. “It could be a game-changer in primary health care settings to support clinicians in stringently diagnosing AOM and guiding treatment decisions.”
“Another benefit of our tool is that the videos we capture can be stored in a patient’s medical record and shared with other providers,” said Hoberman. “We can also show parents and trainees – medical students and residents – what we see and explain why we are or are not making a diagnosis of ear infection. It is important as a teaching tool and for reassuring parents that their child is receiving appropriate treatment.”
Researchers hope their technology can soon be implemented widely across healthcare provider offices to enhance the accurate diagnosis of AOM and support treatment decisions.
Journal reference:
- Nader Shaikh, Shannon J. Conway, Jelena Kovačević, et al. Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children. JAMA Pediatrics, 2024; DOI: 10.1001/jamapediatrics.2024.0011