AIMC Topic: Hearing Loss

Clear Filters Showing 31 to 40 of 58 articles

AutoAudio: Deep Learning for Automatic Audiogram Interpretation.

Journal of medical systems
Hearing loss is the leading human sensory system loss, and one of the leading causes for years lived with disability with significant effects on quality of life, social isolation, and overall health. Coupled with a forecast of increased hearing loss ...

Visual Speech Recognition: Improving Speech Perception in Noise through Artificial Intelligence.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVES: To compare speech perception (SP) in noise for normal-hearing (NH) individuals and individuals with hearing loss (IWHL) and to demonstrate improvements in SP with use of a visual speech recognition program (VSRP).

The Hearing Impairment Ontology: A Tool for Unifying Hearing Impairment Knowledge to Enhance Collaborative Research.

Genes
Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inability to detect sound in one or both ears. This diverse pathology is associated with a myriad of phenotypic expressions and can be non-syndromic or sy...

Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.

PloS one
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...

Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke.

Environmental science and pollution research international
The use of pesticides has been increasing in agriculture, leading to a public health problem. The aim of this study was to evaluate ototoxic effects in farmers who were exposed to cigarette smoke and/or pesticides and to identify possible classificat...

The design and validation of a hybrid digital-signal-processing plug-in for traditional cochlear implant speech processors.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cochlear implants (CIs) are electronic devices restoring partial hearing to deaf individuals with profound hearing loss. In this paper, a new plug-in for traditional IIR filter-banks (FBs) is presented for cochlear implants ...

Cascade recurring deep networks for audible range prediction.

BMC medical informatics and decision making
BACKGROUND: Hearing Aids amplify sounds at certain frequencies to help patients, who have hearing loss, to improve the quality of life. Variables affecting hearing improvement include the characteristics of the patients' hearing loss, the characteris...

Fractionated stereotactic radiation therapy for vestibular schwannomas: Dosimetric factors predictive of hearing outcomes.

Practical radiation oncology
PURPOSE: To determine dosimetric factors predictive of hearing loss in vestibular schwannoma (VS) patients treated with definitive fractionated stereotactic radiation therapy (FSRT), and to report tumor control, serviceable hearing preservation, and ...

Machine learning reveal shared diagnostic biomarkers and convergent pathways in age-related hearing loss and sarcopenia.

Medicine
Age-related hearing loss (HL) and sarcopenia (ARS) are prevalent geriatric syndromes sharing common risk factors. This study aimed to identify shared biomarkers and elucidate convergent pathogenic mechanisms. Transcriptomic datasets were obtained fro...

Digitizing audiograms with deep learning: structured data extraction and pseudonymization for hearing big data.

Hearing research
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...