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Otoscopy

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Detecting tympanostomy tubes from otoscopic images via offline and online training.

Computers in biology and medicine
Tympanostomy tube placement has been commonly used nowadays as a surgical treatment for otitis media. Following the placement, regular scheduled follow-ups for checking the status of the tympanostomy tubes are important during the treatment. The comp...

Otoscopic diagnosis using computer vision: An automated machine learning approach.

The Laryngoscope
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 machine learning approach to build a computer vision algorithm for...

Building an Otoscopic screening prototype tool using deep learning.

Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale
BACKGROUND: Otologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of th...

Artificial intelligence to detect tympanic membrane perforations.

The Journal of laryngology and otology
OBJECTIVE: To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.

Deep Learning for Classification of Pediatric Otitis Media.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope.

Machine Learning for Accurate Intraoperative Pediatric Middle Ear Effusion Diagnosis.

Pediatrics
OBJECTIVES: Misdiagnosis of acute and chronic otitis media in children can result in significant consequences from either undertreatment or overtreatment. Our objective was to develop and train an artificial intelligence algorithm to accurately predi...

Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach.

Scientific reports
Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains c...

Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
AI relates broadly to the science of developing computer systems to imitate human intelligence, thus allowing for the automation of tasks that would otherwise necessitate human cognition. Such technology has increasingly demonstrated capacity to outp...

A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVES: To develop a multiclass-classifier deep learning model and website for distinguishing tympanic membrane (TM) pathologies based on otoscopic images.