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

Sound Intensity Level00:53

Sound Intensity Level

5.1K
Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
5.1K
Hearing01:31

Hearing

58.5K
When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
58.5K
Auditory Perception01:17

Auditory Perception

1.4K
The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Exploring electrode montasges to optimize the non-invasive clinical recording of auditory evoked AP/wave I.

Clinical neurophysiology practice·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

Pre-Neural Source of the Envelope-Following Response Revealed in Cases of Auditory Neuropathy.

Ear and hearing·2026
Same author

Serum procalcitonin: A novel tumor biomarker for diagnosis and disease monitoring in fibrolamellar hepatocellular carcinoma.

Journal of hepatology·2026
Same author

Bridging the gap between physics and biology of hearing: Timing and amplification.

Current opinion in neurobiology·2025
Same author

Serum Procalcitonin: A Novel Tumor Biomarker for Diagnosis and Follow-Up in Fibrolamellar Hepatocellular Carcinoma.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Mar 21, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

29.8K

From Hearing Patterns to Functional Outcomes: Quantifying Audiometric Profiles for Precision Hearing Care.

Perrine Morvan1,2, Marta Campi3, Guillaume Staerman4

  • 1Université Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, Paris, France, perrine.morvan@hotmail.fr.

Audiology & Neuro-Otology
|March 19, 2026
PubMed
Summary
This summary is machine-generated.

Audiogram shape significantly improves hearing aid outcome predictions beyond pure-tone average severity. This audiogram classification guides personalized interventions for better hearing rehabilitation.

Keywords:
AudiologyAudiometric profilesHearing aid fittingSHAP value analysisXGBoost

More Related Videos

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

919
Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss
09:44

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Published on: January 25, 2016

19.9K

Related Experiment Videos

Last Updated: Mar 21, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
14:05

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

29.8K
Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

919
Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss
09:44

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Published on: January 25, 2016

19.9K

Area of Science:

  • Audiology
  • Speech Perception
  • Hearing Loss Heterogeneity

Background:

  • Hearing aid (HA) outcomes vary despite similar hearing loss (HL) severity.
  • Pure-tone average (PTA) measures may not capture clinically relevant patient differences.
  • Audiogram shape (hearing threshold configuration) may improve outcome prediction.

Purpose of the Study:

  • Investigate if audiogram shape improves prediction of speech perception outcomes.
  • Determine if audiogram shape offers predictive value beyond traditional HL severity measures.
  • Assess the impact of audiogram shape on hearing aid fitting and rehabilitation.

Main Methods:

  • Retrospective analysis of 22,694 adults fitted with HAs.
  • Defined 8 audiometric profiles based on HL configuration.
  • Used machine learning to predict speech reception threshold improvements in quiet (SRTQ) and noise (SRTN).
  • Compared model performance with and without audiogram profile membership as a predictor.

Main Results:

  • 93% of participants showed meaningful SRTQ and/or SRTN improvement.
  • Adding audiogram profile membership increased prediction accuracy by 5-6%.
  • Distinct optimization pathways identified for different audiogram profiles (e.g., Moderate Presbycusis, Ski Slope, Advanced Presbycusis).
  • Mid-to-high frequencies (2-6 kHz) impacted speech-in-noise; low frequencies (250-1000 Hz) and support impacted speech-in-quiet.

Conclusions:

  • Audiogram shape provides critical predictive information beyond HL severity.
  • Patient classification by audiogram shape enables identification of most effective interventions.
  • Profile-guided approach facilitates personalized treatment decisions and efficient resource allocation.