Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Objective Comparison of Auditory Profiles Using Manifold Learning and Intrinsic Measures.

Trends in hearing·2026
Same author

Calibration offset estimation in mobile hearing tests via categorical loudness scaling.

International journal of audiology·2026
Same author

A cross-domain test battery for comprehensive hearing loss characterisation using functional, physiological, and vestibular measures.

International journal of audiology·2026
Same author

Audio quality predictions correlate with perception of processing delays across different simulated hearing device conditions.

JASA express letters·2026
Same author

Standard audiogram classification from loudness scaling data using unsupervised, supervised, and explainable machine learning techniques.

International journal of audiology·2026
Same author

Perceptual measures of normal-hearing and hearing-impaired listeners across defined virtual acoustic scenes.

International journal of audiology·2026

Related Experiment Video

Updated: Dec 8, 2025

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion
03:58

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

715

Sensitivity and specificity of automatic audiological classification using expert-labelled audiological data and

Mareike Buhl1,2, Anna Warzybok1,2, Marc René Schädler1,2

  • 1Medizinische Physik, Universität Oldenburg, Oldenburg, Germany.

International Journal of Audiology
|September 18, 2020
PubMed
Summary

Machine learning aids audiology by evaluating diagnostic tools. Common Audiological Functional Parameters (CAFPA) effectively represent auditory deficiencies, matching the performance of multiple audiological measures.

Keywords:
Audiological diagnosticsROC analysisclassificationmachine learning

More Related Videos

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published on: September 7, 2022

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

748

Related Experiment Videos

Last Updated: Dec 8, 2025

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion
03:58

Enhancing Electrode Location Assessment in Cochlear Implantation via Computed Tomography Image Fusion

Published on: January 17, 2025

715
Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

Published on: September 7, 2022

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

748

Area of Science:

  • Audiology
  • Machine Learning
  • Medical Informatics

Background:

  • Development of machine learning-based audiological diagnostic tools requires evaluation of various measures.
  • Common Audiological Functional Parameters (CAFPAs) offer an integrated approach to represent audiological data from diverse clinical databases.

Purpose of the Study:

  • To evaluate the classification performance of audiological measures and CAFPAs for audiological diagnostics.
  • To assess the efficacy of CAFPAs as abstract representations of auditory deficiencies.

Main Methods:

  • Classification performance analysis using sensitivity and specificity.
  • Evaluation on a dataset of 287 cases from a previous study with expert-labeled data.
  • Comparison of classification performance between individual audiological measures, combinations of measures, and CAFPAs.

Main Results:

  • A combination of four to six audiological measures achieved maximum classification performance, indicating redundancy in individual measures.
  • CAFPAs demonstrated comparable classification performance to optimal sets of audiological measures across various diagnostic questions.
  • The utility of weighted parameter combinations for audiological diagnostic questions was highlighted.

Conclusions:

  • The concept of CAFPAs as compact, abstract representations of auditory deficiencies is validated.
  • CAFPAs provide a reliable and efficient method for integrating audiological data and supporting diagnostic tools.
  • Machine learning approaches can be effectively supported by well-defined audiological parameters for improved diagnostic accuracy.