Objectives To summarise the accuracy of artificial intelligence (AI) computer vision
algorithms to classify ear disease from otoscopy. Design Systematic review and meta …

Methods| We analyzed postoperative patient instructions for 8 common pediatric
otolaryngologic procedures: tympanostomy tube placement, tonsillectomy and …

Objective To systematically evaluate the development of Machine Learning (ML) models
and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) …

Objective: To develop an artificial intelligence image classification algorithm to triage
otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander …

The present study aimed to develop a machine learning network to diagnose middle ear
diseases with tympanic membrane images and to identify its assistive role in the diagnostic …

A middle ear infection is a prevalent inflammatory disease most common in the pediatric
population, and its financial burden remains substantial. Current diagnostic methods are …

Objective We compared the diagnostic performance of human clinicians with that of a neural
network algorithm developed using a library of tympanic membrane images derived from …

Telehealth in otolaryngology is gaining popularity as a potential tool for increased access for
rural populations, decreased specialist wait times, and overall savings to the healthcare …

Otolaryngological diagnoses, such as otitis media, are traditionally performed using
endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The …

Introduction Acute otitis media (AOM) affects most (80%) children by 5 years of age and is
the most common reason children are prescribed antibiotics. The epidemiology of AOM has …