AIMC Topic: Ear Diseases
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Deep learning multi-classification of middle ear diseases using synthetic tympanic images.
Acta oto-laryngologica
Jan 10, 2025
BACKGROUND: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.
Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Jan 10, 2024
PURPOSE: Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to...
Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.
Annals of biomedical engineering
Dec 14, 2023
Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. ...
Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.
Scientific reports
Apr 1, 2023
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 external performance. 1842 otoscopic images were collected from three...
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
Sep 14, 2021
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...
Assessing outcomes of ear molding therapy by health care providers and convolutional neural network.
Scientific reports
Sep 9, 2021
Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we comp...
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
Nov 26, 2019
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...
Otoscopic diagnosis using computer vision: An automated machine learning approach.
The Laryngoscope
Sep 18, 2019
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...
Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database.
EBioMedicine
Jul 1, 2019
BACKGROUND: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may pla...
Model-based hearing diagnostics based on wideband tympanometry measurements utilizing fuzzy arithmetic.
Hearing research
Feb 28, 2019
Today's audiometric methods for the diagnosis of middle ear disease are often based on a comparison of measurements with standard curves, that represent the statistical range of normal hearing responses. Because of large inter-individual variances in...
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