Catalyst Awardee

Project Description

Early detection of Age-related Hearing Loss in Artificial Intelligence

Chung-Yen Lin, PhD| Institute of Information Science, Academia Sinica; Taso Yu, PhD
Competition Sponsor: Academia Sinica
Awardee Year: 2023

The World Health Organization predicts that 1.2 billion adults over 60 will have age-related hearing loss by 2025, associated with frailty and dementia. To combat such a dilemma about age-related hearing loss (presbycusis), we develop an AI model which can detect hearing loss early using data from the Taiwanese population’s genetic background, lifestyle, medical treatment, and environment based on the dataset from Taiwan Biobank. The AI model aims to detect hearing loss early by analyzing genetic variance (SNPs), lifestyle, medical treatment, and environmental factors as input and hearing loss/tinnitus as output. The study uses the Taiwanese population dataset from Taiwan Biobank, making it the first in the country. This modeling approach can be applied globally to detect hearing loss and other age-related diseases such as osteoporosis, sarcopenia, disability, cognitive decline, and dementia. Further analysis of the AI models may uncover more biomarkers related to hearing loss and lead to new therapies and drug candidates. This approach could ultimately improve our quality of life as we age.

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