An artificial intelligence (AI) model built at Mass Eye and Ear was shown to be significantly more accurate than doctors at diagnosing pediatric ear infections in the first head-to-head evaluation of its kind, a research team working to develop the model for clinical use reported. According to a new study published August 16 in Otolaryngology–Head and Neck Surgery, the model, called OtoDX, was more than 95 percent accurate in diagnosing an ear infection in a set of 22 test images compared to 65 percent accuracy among a group of clinicians consisting of ENTs, pediatricians and primary care doctors, who reviewed the same images. When tested in a dataset of more than 600 inner ear images, the AI model had a diagnostic accuracy of more than 80 percent, representing a significant leap over the average accuracy of clinicians reported in medical literature. The model utilizes a type of AI called deep learning and was built from hundreds of photographs collected from children prior to undergoing surgery at Mass Eye and Ear for recurrent ear infections or fluid in the ears. The results signify a major step towards the development of a diagnostic tool that can one day be deployed to clinics to assist doctors during patient evaluations, according to the authors.

According to a new study published August 16 in Otolaryngology-Head and Neck Surgery, the model, called OtoDX, was more than 95 percent accurate in diagnosing an ear infection in a set of 22 test images compared to 65 percent accuracy among a group of clinicians consisting of ENTs, pediatricians and primary care doctors, who reviewed the same images. When tested in a dataset of more than 600 inner ear images, the AI model had a diagnostic accuracy of more than 80 percent, representing a significant leap over the average accuracy of clinicians reported in medical literature. The model utilizes a type of AI called deep learning and was built from hundreds of photographs collected from children prior to undergoing surgery at Mass Eye and Ear for recurrent ear infections or fluid in the ears. The results signify a major step towards the development of a diagnostic tool that can one day be deployed to clinics to assist doctors during patient evaluations, according to the authors. An AI-based diagnostic tool can give providers, like pediatricians and urgent care clinics, an additional test to better inform their clinical decision-making.

A child pulling his or her ear in pain typically results in a parent scheduling a doctor's appointment to check for a medical problem. However, studies have consistently shown that it's a coin toss whether or not a pediatrician or a non-specialist can correctly diagnose an ear infection. "The ears are just a nice and easy thing about which to say, 'This is why my child isn't sleeping,'" says James Clark, M.B., B.Ch., B.A.O., assistant professor of otolaryngology--head and neck surgery at the Johns Hopkins University School of Medicine. Incorrect diagnoses can be frustrating and time-consuming for parents, not to mention the ear, nose and throat doctors called in for a second opinion. Clark and Therese Canares, M.D., assistant professor in pediatric emergency medicine at the Johns Hopkins University School of Medicine, believe artificial intelligence (AI) could help better diagnose and manage ear infections, even remotely from a patient's home.

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.

Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.