ReviewSeminars in hearing2022
Translational Applications of Machine Learning in Auditory Electrophysiology.
Review in Seminars in hearing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Effect of Lateralization, Age, and Sex on Frequency Following Response in Children: Neural Speech Encoding to a 170 ms [da] Stimulus.Life (Basel, Switzerland) · 2026Article
- 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 · 2025Article
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- Objective discrimination of bimodal speech using frequency following responses.Hearing research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Machine learning (ML) is transforming nearly every aspect of modern life including medicine and its subfields, such as hearing science. This article presents a brief conceptual overview of selected ML approaches and describes how these techniques are being applied to outstanding problems in hearing science, with a particular focus on auditory evoked potentials (AEPs). Two vignettes are presented in which ML is used to analyze subcortical AEP data. The first vignette demonstrates how ML can be used to determine if auditory learning has influenced auditory neurophysiologic function. The second vignette demonstrates how ML analysis of AEPs may be useful in determining whether hearing devices are optimized for discriminating speech sounds.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.