AIMC Topic: Audiometry, Pure-Tone

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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.

Scientific reports Mar 22, 2025
Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. The subtle changes in hearing often go unnoticed, presenting a challen...
Machine Learning Algorithms Female Risk Factors Humans Middle Aged Nutrition Surveys Heart Disease Risk Factors Audiometry, Pure-Tone Hearing Loss Aged Adult Male Cardiovascular Diseases
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Feature-Based Audiogram Value Estimator (FAVE): Estimating Numerical Thresholds from Scanned Images of Handwritten Audiograms.

Journal of medical systems Feb 27, 2025
Hearing loss is a public health concern that affects millions of people globally. Clinically, a person's hearing sensitivity is often measured using pure-tone audiometry and visualized as a pure-tone audiogram, a plot of hearing sensitivity as a func...
Longitudinal Studies Audiometry, Pure-Tone Hearing Loss Humans Machine Learning Electronic Health Records
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Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Aug 28, 2024
OBJECTIVE: To apply machine learning models based on air conduction thresholds of pure-tone audiometry for automatic diagnosis of Meniere's disease (MD) and prediction of endolymphatic hydrops (EH).
Middle Aged Magnetic Resonance Imaging Audiometry, Pure-Tone Meniere Disease Endolymphatic Hydrops Aged Adult Sensitivity and Specificity Male Humans Machine Learning Retrospective Studies Female
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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...
Hearing Loss, Conductive Middle Aged Audiometry, Pure-Tone Hearing Loss Hearing Loss, Sensorineural Humans Neural Networks, Computer Female Adult Male
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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 ...
Humans Female Aged Deep Learning Adolescent Audiometry, Pure-Tone Hearing Loss Evoked Potentials, Auditory, Brain Stem Young Adult Adult Male Middle Aged
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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...
Humans Retrospective Studies Female Aged Child, Preschool Middle Aged Child Aged, 80 and over Deep Learning Audiometry, Pure-Tone China Young Adult Hearing Loss Adolescent Adult Male
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Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.

The Journal of laryngology and otology May 9, 2024
OBJECTIVES: This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy.
Hearing Loss, Noise-Induced Metallurgy Noise, Occupational Predictive Value of Tests Tinnitus Audiometry, Pure-Tone Occupational Diseases Male Middle Aged Occupational Exposure Support Vector Machine Female Risk Factors Humans Adult Machine Learning
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Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.

Ear and hearing Mar 29, 2022
OBJECTIVES: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movemen...
Otitis Media Otoscopy Otosclerosis Hearing Loss, Conductive Adult Deep Learning Otitis Media with Effusion Audiometry, Pure-Tone Humans Retrospective Studies
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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 ...
Audiometry, Pure-Tone Aged Hearing Aged, 80 and over Auditory Threshold Hearing Loss Male Republic of Korea Child, Preschool Middle Aged Child Discrimination Learning Female Infant Computational Biology Infant, Newborn Speech Reception Threshold Test Adolescent Models, Statistical Reproducibility of Results Speech Perception Humans Neural Networks, Computer Machine Learning Algorithms Young Adult Adult
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The Use of a Robot to Insert an Electrode Array of Cochlear Implants in the Cochlea: A Feasibility Study and Preliminary Results.

Audiology & neuro-otology Apr 26, 2021
INTRODUCTION: Cochlear implants (CIs) are commonly used for the rehabilitation of profound bilateral hearing loss. However, patients with substantial residual acoustic hearing are potential CI candidates. Because of both improvements in technology an...
Cochlea Electrodes, Implanted Robotics Feasibility Studies Audiometry, Pure-Tone Humans Cochlear Implantation Cochlear Implants
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