AIMC Topic: Tympanic Membrane
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Deep learning algorithm classification of tympanostomy tube images from a heterogenous pediatric population.
International journal of pediatric otorhinolaryngology
Mar 13, 2025
IMPORTANCE: The ability to augment routine post-operative tube check appointments with at-home digital otoscopes and deep learning AI could improve health care access as well as reduce financial and time burden on families.
Cooperative GAN: Automated tympanic membrane anomaly detection using a Cooperative Observation Network.
Computer methods and programs in biomedicine
Feb 23, 2025
BACKGROUND AND OBJECTIVES: Recently, artificial intelligence (AI) has been applied to otolaryngology. However, existing supervised learning methods cannot easily predict data outside the learning domain. Moreover, collecting diverse medical data has ...
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.
Intelligent Song Recognition via a Hollow-Microstructure-Based, Ultrasensitive Artificial Eardrum.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sep 20, 2024
Artificial ears with intelligence, which can sensitively detect sound-a variant of pressure-and generate consciousness and logical decision-making abilities, hold great promise to transform life. However, despite the emerging flexible sensors for sou...
Pediatric tympanostomy tube assessment via deep learning.
American journal of otolaryngology
Apr 28, 2024
PURPOSE: Tympanostomy tube (TT) placement is the most frequently performed ambulatory surgery in children under 15. After the procedure it is recommended that patients follow up regularly for "tube checks" until TT extrusion. Such visits incur direct...
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...
A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.
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
Oct 13, 2022
BACKGROUND: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images.
Automated multi-class classification for prediction of tympanic membrane changes with deep learning models.
PloS one
Oct 10, 2022
BACKGROUNDS AND OBJECTIVE: Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a databa...
Ultrathin Eardrum-Inspired Self-Powered Acoustic Sensor for Vocal Synchronization Recognition with the Assistance of Machine Learning.
Small (Weinheim an der Bergstrasse, Germany)
Feb 5, 2022
With the rapid development of human-machine interfaces, artificial acoustic sensors play an important role in the hearing impaired. Here, an ultrathin eardrum-like triboelectric acoustic sensor (ETAS) is presented consisting of silver-coated nanofibe...
EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images.
Artificial intelligence in medicine
Apr 8, 2021
This paper presents a method for automatic segmentation of tympanic membranes (TMs) from video-otoscopic images based on deep fully convolutional neural network. Built upon the UNet architecture, the proposed EAR scheme is based on three main paradig...
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