This proposal aims to leverage a large-scale, anonymized patient database to evaluate the real-world outcomes of hearing aid use through advanced artificial intelligence (AI) and machine learning techniques. By analyzing vast and diverse datasets—encompassing audiometric data, patient-reported outcome measures (PROMs), hearing aid settings, and demographic information—we will develop predictive models to identify the key determinants of successful hearing aid adoption and satisfaction. The core of this initiative is to move beyond traditional, one-size-fits-all approaches and towards a data-driven, personalized framework for audiological care. The AI will uncover complex patterns and correlations that are not apparent through conventional analysis, providing clinicians with powerful tools to optimize hearing aid fittings and counseling from the outset.

How to predict Hearing Aid Outcomes Using AI

This session explores the cutting-edge frontier of inner ear therapeutics. We will cover groundbreaking innovations, including gene therapy for hereditary deafness, regenerative medicine for hair cell restoration, and novel drug delivery systems for treating hearing and balance disorders.

Outcome Objectives Participants will be able to:

1. Describe emerging gene and cell-based strategies for hearing restoration.

2. Evaluate the potential of next-generation cochlear and vestibular implants.

3. Identify new pharmacological targets and delivery methods for the inner ear.

Background Sensorineural hearing loss and vestibular disorders have long been considered irreversible. Advances in molecular biology and bioengineering are now challenging this paradigm, creating unprecedented opportunities for biological repair and functional restoration of the inner ear.

IFOS educational courses around the world will be discussed.

Randomised Control Trial Comparing A New Algorithm to Improved Discrimination in Noise