Objective To update the literature and provide a systematic review of image‐based artificial
intelligence (AI) applications in otolaryngology, highlight its advances, and propose future …

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

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 …

Medical image classification is a very fundamental and crucial task in the field of computer
vision. These years, CNN-based and Transformer-based models are widely used in …

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 …

Otitis media with effusion (OME) is fluid accumulation in the middle ear without signs of
systemic infection. OME can cause hearing loss, ear fullness, speech retardation, and a …

Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of
ear diseases extracted from the knowledge of otolaryngologists is limited. Improved …

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

Objectives To identify and review key research advances from the literature published
between 2019 and 2023 on the diagnosis and microbiology of otitis media (OM) including …

A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which
significantly increases the difficulty of treating the disease and the likelihood of serious …