AIMC Topic: Audiometry, Pure-Tone

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

Scientific reports
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...

Feature-Based Audiogram Value Estimator (FAVE): Estimating Numerical Thresholds from Scanned Images of Handwritten Audiograms.

Journal of medical systems
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...

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
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).

Automated hearing loss type classification based on pure tone audiometry data.

Scientific reports
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
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 ...

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
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...

Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.

The Journal of laryngology and otology
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.

Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.

Ear and hearing
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...

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
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...