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Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.
Sensors (Basel)
November 2024
Phoniatris and Audiology Unit, Department of Neuroscience DNS, University of Padova, 33100 Treviso, Italy.
Article Synopsis
- The integration of AI is significantly changing healthcare, including audiology, and this review aims to inform practitioners and stakeholders about its potential and challenges.
- A comprehensive search of medical literature revealed 1,359 articles, out of which 104 were included, highlighting a notable increase in research on AI in audiology, with 87.5% of studies published in the last four years.
- AI techniques are being used for various applications in audiology, such as automated audiometry, clinical predictions, radiological image analysis, and generating diagnostic reports, but ethical concerns and the need for diverse data collection remain challenges in the field.
Automated Measurement of Speech Recognition, Reaction Time, and Speech Rate and Their Relation to Self-Reported Listening Effort for Normal-Hearing and Hearing-Impaired Listeners Using various Maskers.
Trends Hear
September 2024
Cluster of Excellence Hearing4all, Oldenburg, Germany.
In speech audiometry, the speech-recognition threshold (SRT) is usually established by adjusting the signal-to-noise ratio (SNR) until 50% of the words or sentences are repeated correctly. However, these conditions are rarely encountered in everyday situations. Therefore, for a group of 15 young participants with normal hearing and a group of 12 older participants with hearing impairment, speech-recognition scores were determined at SRT and at four higher SNRs using several stationary and fluctuating maskers.
View Article and Find Full Text PDFApplications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.
Otolaryngol Head Neck Surg
January 2025
Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China.
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).
Study Design: Retrospective study.
Setting: Tertiary medical center.
Quick Estimation of Minimum Hearing Levels Using a Binaural Multifrequency Stimulus Paradigm: Proof of Concept.
Ear Hear
October 2024
Intelligent Hearing Systems, Miami, Florida, USA.
Objectives: Objective estimation of minimum hearing levels using auditory brainstem responses (ABRs) elicited by single frequency tone-bursts presented monaurally is currently considered the gold standard. However, the data acquisition time to estimate thresholds (for both ears across four audiometric frequencies) using this method usually exceeds the sleep time (ranging between 35 and 49 minutes) in infants below 4 months, thus providing incomplete information of hearing status which in turn delays timely clinical intervention. Alternate approaches using faster rate, or tone-burst trains have not been readily accepted due to additional hardware and software requirements.
View Article and Find Full Text PDFAutomatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework.
Otolaryngol Head Neck Surg
October 2024
Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
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 ABR waveforms in participants with varying ages and hearing levels.
Study Design: The research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants.
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