13. Graph Neural Networks for the Detection of Missing Cochlear Hair Cells

Hearing loss affects over 1.5 billion people globally, representing approximately 20% of the world's population. Currently, 430 million individuals have disabling hearing loss, a number expected to rise to 700 million by 2050. Children with hearing loss often receive lower-quality education compared to their peers, and adults frequently experience higher unemployment rates or occupy lower-level jobs. According to the World Health Organization, unaddressed hearing loss results in an annual global economic burden of approximately US\$ 980 billion, encompassing healthcare costs, educational support, lost productivity, and broader societal impacts. Cochlear hair cell loss significantly impairs the conversion of sound into neural signals. To address this critical issue, we propose a novel, automated deep learning approach utilizing Graph Neural Networks (GNNs) to detect missing cochlear hair cells. In our approach, individual hair cells are represented as nodes within a graph, with edges capturing spatial and morphological relationships. This allows GNNs to effectively learn and identify complex patterns associated with hair cell loss and degeneration, providing improved accuracy and efficiency compared to traditional manual morphometric analyses. Our methodology offers a scalable and robust framework for advancing hearing-loss diagnostics and research.

Graph Neural Networks

Cochlear Hair Cells Detection 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025