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Otitis Media

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Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object localization, semantic segmentation, and object instance segmentation. Accordingly, the applicabili...

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.

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...

[Research on computer aided diagnosis of otitis media based on faster region convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Otitis media is one of the common ear diseases, and its accurate diagnosis can prevent the deterioration of conductive hearing loss and avoid the overuse of antibiotics. At present, the diagnosis of otitis media mainly relies on the doctor's visual i...

Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.

Ear and hearing
OBJECTIVES: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movemen...

A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.

JAMA otolaryngology-- head & neck surgery
IMPORTANCE: Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However,...

A Deep Learning Approach for Detecting Otitis Media From Wideband Tympanometry Measurements.

IEEE journal of biomedical and health informatics
OBJECTIVE: In this study, wepropose an automatic diagnostic algorithm for detecting otitis media based on wideband tympanometry measurements.

"Human vs Machine" Validation of a Deep Learning Algorithm for Pediatric Middle Ear Infection Diagnosis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
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 children taken to the operating room with the intent of performing myring...

Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Otitis media (OM) is a model disease for developing, validating, and implementing artificial intelligence (AI) techniques. We aim to review the state of the art applications of AI used to diagnose OM in pediatric and adult populations.