Assigning individuals with hearing impairment to auditory profiles can support a better understanding of the causes and consequences of hearing loss and facilitate profile-based hearing-aid fitting. However, the factors influencing auditory profile generation remain insufficiently understood, and existing profiling frameworks have rarely been compared systematically....
No actionable change — this is a methodological study introducing a new analytical framework; clinical utility has not yet been validated in practice.
If validated, data-driven auditory profiling could move audiology beyond traditional audiogram categories toward more precise, individualized classification of hearing loss.
- 01Manifold learning applied to auditory profiles to enable objective, data-driven comparison across patients.
- 02Intrinsic measures used to capture the natural geometry of hearing-loss data without relying on predefined categories.
- 03Study published in Trends in Hearing, a peer-reviewed audiology journal.
- 04Potential to improve hearing-loss classification beyond standard pure-tone audiogram descriptors.
- 05Findings are preliminary and require external validation before clinical adoption.
Manifold learning and intrinsic measures can objectively compare auditory profiles in individuals with hearing impairment.
studypartially supportedThe approach improves classification and understanding of hearing loss compared to conventional methods.
studyunclear- PMID
- 42383377
- DOI
- 10.1177/23312165261461348.
- Journal
- Trends in Hearing
- Publication type
- research_article
- Evidence level
- 4
- Population
- Individuals with hearing impairment
- Intervention
- Manifold learning and intrinsic measures for auditory profile comparison
Primary outcomes
Objective comparison of auditory profiles; Classification accuracy of hearing loss patterns