AIMC Topic: Hearing Loss
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Artificial intelligence enabled smart mask for speech recognition for future hearing devices.
Scientific reports
Dec 3, 2024
In recent years, Lip-reading has emerged as a significant research challenge. The aim is to recognise speech by analysing Lip movements. The majority of Lip-reading technologies are based on cameras and wearable devices. However, these technologies h...
Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.
Work (Reading, Mass.)
Nov 25, 2024
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
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...
Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Jun 1, 2024
OBJECTIVE: Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ...
Deep learning-based auditory attention decoding in listeners with hearing impairment.
Journal of neural engineering
May 22, 2024
This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-n...
Pure tone audiogram classification using deep learning techniques.
Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
May 15, 2024
OBJECTIVE: Pure tone audiometry has played a critical role in audiology as the initial diagnostic tool, offering vital insights for subsequent analyses. This study aims to develop a robust deep learning framework capable of accurately classifying aud...
A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population.
BMC public health
Apr 25, 2024
BACKGROUND: Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in communit...
Understanding, experience and attitudes towards artificial intelligence technologies for clinical decision support in hearing health: a mixed-methods survey of healthcare professionals in the UK.
The Journal of laryngology and otology
Apr 18, 2024
OBJECTIVES: Clinician acceptance influences technology adoption, but UK health professionals' attitudes towards artificial intelligence (AI) in hearing healthcare are unclear. This study aimed to address this knowledge gap.
Optical Microphone-Based Speech Reconstruction System With Deep Learning for Individuals With Hearing Loss.
IEEE transactions on bio-medical engineering
Nov 21, 2023
OBJECTIVE: Although many speech enhancement (SE) algorithms have been proposed to promote speech perception in hearing-impaired patients, the conventional SE approaches that perform well under quiet and/or stationary noises fail under nonstationary n...
A Hybrid Deep Learning Approach to Identify Preventable Childhood Hearing Loss.
Ear and hearing
Jun 15, 2023
OBJECTIVE: Childhood hearing loss has well-known, lifelong consequences. Infection-related hearing loss disproportionately affects underserved communities yet can be prevented with early identification and treatment. This study evaluates the utility ...
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