AIMC Topic: Auditory Threshold

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A machine-learning-based approach to predict early hallmarks of progressive hearing loss.

Hearing research Jun 6, 2025
Machine learning (ML) techniques are increasingly being used to improve disease diagnosis and treatment. However, the application of these computational approaches to the early diagnosis of age-related hearing loss (ARHL), the most common sensory def...
Animals Male Disease Progression Mice Age Factors Humans Machine Learning Phenotype Female Early Diagnosis Hearing Auditory Threshold Evoked Potentials, Auditory, Brain Stem Disease Models, Animal Presbycusis Mice, Inbred C57BL Predictive Value of Tests Signal Processing, Computer-Assisted Cadherins
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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.
Hearing Tests Humans Auditory Threshold Deep Learning Cochlea Acoustic Stimulation Otoacoustic Emissions, Spontaneous Hearing Hearing Loss, Sensorineural
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Predicting speech discrimination scores from pure-tone thresholds-A machine learning-based approach using data from 12,697 subjects.

PloS one Dec 31, 2021
Diagnostic tests for hearing impairment not only determines the presence (or absence) of hearing loss, but also evaluates its degree and type, and provides physicians with essential data for future treatment and rehabilitation. Therefore, accurately ...
Models, Statistical Audiometry, Pure-Tone Hearing Hearing Loss Speech Perception Adolescent Child, Preschool Child Computational Biology Auditory Threshold Discrimination Learning Speech Reception Threshold Test Female Infant, Newborn Reproducibility of Results Aged Aged, 80 and over Young Adult Adult Male Middle Aged Infant Humans Neural Networks, Computer Machine Learning Algorithms Republic of Korea
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Robust machine learning method for imputing missing values in audiograms collected in children.

International journal of audiology Feb 27, 2021
OBJECTIVE: To assess the accuracy and reliability of a machine learning (ML) algorithm for predicting the full audiograms of hearing-impaired children relative to the common approach (CA).
Retrospective Studies Reproducibility of Results Child Audiometry, Pure-Tone Auditory Threshold Humans Machine Learning
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From manual to artificial intelligence fitting: Two cochlear implant case studies.

Cochlear implants international Sep 17, 2019
To assess whether CI programming by means of a software application using artificial intelligence (AI), FOX®, may improve cochlear implant (CI) performance. Two adult CI recipients who had mixed auditory results with their manual fitting were selec...
Speech Perception Auditory Threshold Correction of Hearing Impairment Prosthesis Fitting Humans Artificial Intelligence Software Female Aged Male Deafness Cochlear Implants
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Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.

PloS one Jun 3, 2019
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearin...
Male Middle Aged Logistic Models Noise Multivariate Analysis Hearing Loss Auditory Threshold Cochlea Female Aged Humans Aged, 80 and over Machine Learning Young Adult Algorithms Adult
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Objective auditory brainstem response classification using machine learning.

International journal of audiology Jan 21, 2019
OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: 'clear response', 'inconclusive' or 'response absent'.
Humans Databases, Factual Evoked Potentials, Auditory, Brain Stem Brain Stem Deep Learning Female Signal Processing, Computer-Assisted Young Adult Acoustic Stimulation Adult Hearing Tests Male Auditory Threshold
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A neural-based vocoder implementation for evaluating cochlear implant coding strategies.

Hearing research Jan 14, 2016
Most simulations of cochlear implant (CI) coding strategies rely on standard vocoders that are based on purely signal processing techniques. However, these models neither account for various biophysical phenomena, such as neural stochasticity and ref...
Psychoacoustics Electroencephalography Signal Processing, Computer-Assisted Prosthesis Design Adult Speech Perception Humans Cochlear Implantation Acoustic Stimulation Male Neural Networks, Computer Speech Acoustics Stochastic Processes Auditory Pathways Middle Aged Computer Simulation Auditory Threshold Electric Stimulation Time Factors Female Cochlear Implants Voice Quality Persons with Hearing Disabilities Loudness Perception
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Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.

International archives of occupational and environmental health Nov 29, 2014
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
Occupational Diseases Humans Steel Male Audiometry Neural Networks, Computer Occupational Exposure Hearing Loss, Noise-Induced Adult Predictive Value of Tests Metallurgy Cross-Sectional Studies Iran Noise, Occupational Auditory Threshold
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Digitizing audiograms with deep learning: structured data extraction and pseudonymization for hearing big data.

Hearing research Aug 1, 2025
PURPOSE: hearing loss relies on pure-tone audiometry (PTA); however, audiograms are often stored as unstructured images, limiting their integration into electronic medical records (EMRs) and common data models (CDMs). This study developed a deep lear...
Humans Neural Networks, Computer Electronic Health Records Deep Learning Big Data Predictive Value of Tests Audiometry, Pure-Tone Hearing Hearing Loss Auditory Threshold
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