AIMC Topic: Hearing Loss, Sensorineural
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Development and Validation of a Machine Learning Model for Detection and Classification of Vertigo.
The Laryngoscope
Dec 19, 2024
PURPOSE: This study aims to investigate whether artificial intelligence can improve the diagnostic accuracy of vertigo related diseases.
Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss.
Scientific reports
Aug 29, 2024
This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data from 1108 patients with SSNHL between January 2015 and May 2023 were retrospectively analyzed. Patien...
Automated hearing loss type classification based on pure tone audiometry data.
Scientific reports
Jun 20, 2024
Hearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient's hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of...
Machine learning-based longitudinal prediction for GJB2-related sensorineural hearing loss.
Computers in biology and medicine
May 15, 2024
BACKGROUND: Recessive GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for GJB2-related SNHL using machine le...
Three-dimensional heavily T2-weighted FLAIR in the detection of blood-labyrinthine barrier leakage in patients with sudden sensorineural hearing loss: comparison with T1 sequences and application of deep learning-based reconstruction.
European radiology
Jan 17, 2024
OBJECTIVE: Blood-labyrinthine barrier leakage has been reported in sudden sensorineural hearing loss (SSNHL). We compared immediate post-contrast 3D heavily T2-weighted fluid-attenuated inversion recovery (FLAIR), T1 spin echo (SE), and 3D T1 gradien...
Deep Learning Models for Predicting Hearing Thresholds Based on Swept-Tone Stimulus-Frequency Otoacoustic Emissions.
Ear and hearing
Nov 22, 2023
OBJECTIVES: This study aims to develop deep learning (DL) models for the quantitative prediction of hearing thresholds based on stimulus-frequency otoacoustic emissions (SFOAEs) evoked by swept tones.
Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review.
The Annals of otology, rhinology, and laryngology
Oct 20, 2023
BACKGROUND: Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease...
Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss.
eLife
May 10, 2023
Listeners with hearing loss often struggle to understand speech in noise, even with a hearing aid. To better understand the auditory processing deficits that underlie this problem, we made large-scale brain recordings from gerbils, a common animal mo...
Audiological outcomes of robot-assisted cochlear implant surgery.
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
Apr 12, 2023
PURPOSE: The main objective of this study is to evaluate the short-term and long-term audiological outcomes in patients who underwent cochlear implantation with a robot-assisted system to enable access to the cochlea, and to compare outcomes with a m...
Restoring speech intelligibility for hearing aid users with deep learning.
Scientific reports
Feb 15, 2023
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep l...
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