Speech-in-noise screening identifies children missed by traditional methods, with electrophysiologic findings revealing underlying neural encoding deficits.
This presentation details the development and community review of openEHR archetypes for three common audiological tests — loudness scaling, speech tests, and audiogram — highlighting the modelling process and the importance of integrating clinical, research, and data perspectives to achieve widely accepted audiology data standards.
This presentation showcases the latest progress in community-driven data standards in audiology, highlighting key developments in both openEHR and OMOP frameworks that aim to unlock interoperable big audiological data for AI-driven research and improved patient care.
Despite its high reliability, the Audiometric Weber Test demonstrates insufficient clinical validity, suggesting it should not be recommended as a primary diagnostic tool for verifying hearing thresholds in routine practice.
Hearing Data, the tool that unlocks the potential of the Noah database to enhance patient care and clinical research.
This perspective highlights emerging research directions to improve the ecological validity of hearing assessment by bridging laboratory testing and real-world listening conditions.
The Panoramic ECAP Method is accessible online (now with 35+ users) to allow researchers and clinicians alike to collect the relevant Electrically Evoked Compound Action Potentials and subsequently extract patient-specific estimates of current spread and neural responsiveness for cochlear-implant users.