This study seeks to address the following research questions:
Can machine learning (ML) and artificial intelligence (AI) models effectively predict the onset, progression, or severity of tinnitus and hearing loss using multimodal data from the UK Biobank?
Which biological, lifestyle, or environmental features within the dataset are most predictive of these auditory conditions?
How do different ML/AI algorithms compare in performance for this prediction task, and can they uncover novel risk factors or interactions?
The primary aims are to:
Develop and validate ML/AI models to predict tinnitus and hearing loss using the UK Biobank’s comprehensive datasets (e.g., audiometric, genetic, demographic, biology, MRI and lifestyle data).
Identify key predictive biomarkers and risk factors to enhance understanding of disease mechanisms.
Compare algorithm performance (e.g., deep learning vs. traditional models) to determine optimal approaches for clinical translation.
Generate interpretable tools to aid early diagnosis and personalized interventions.
Data-Only Application Compliance
This application requests access to existing UK Biobank data only and does not involve sample collection, re-contact, or depletion of limited resources. By leveraging pre-collected datasets, the study aligns with streamlined access procedures and avoids the ethical and logistical complexities of sample/re-contact requests. The proposed analysis adheres to UK Biobank’s governance framework, ensuring responsible use of anonymized data. Outcomes aim to advance precision medicine for auditory disorders while maintaining compliance with the Biobank’s data-access policies.