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

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

Background Otitis media is one of the most common childhood diseases worldwide, but
because of lack of doctors and health personnel in developing countries it is often …

Background: Otitis media includes several common inflammatory conditions of the middle
ear that can have severe complications if left untreated. Correctly identifying otitis media can …

OBJECTIVE:: To determine if digitised still eardrum images, with a clinical history, and
audiometry and tympanometry data provide sufficient information to an ear specialist to …

Objective Access to otolaryngology is limited by lengthy wait lists and lack of specialists,
especially in rural and remote areas. The objective of this study was to use an automated …

OBJECTIVES: Misdiagnosis of acute and chronic otitis media in children can result in
significant consequences from either undertreatment or overtreatment. Our objective was to …

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

Background Middle ear diseases such as otitis media and middle ear effusion, for which
diagnoses are often delayed or misdiagnosed, are among the most common issues faced by …

To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep
learning methods to identify middle ear disease from otoscopic images, between internal to …