Automated Phenotypic Classification of iPSC-Derived Inner Ear Organoids using Multi-Modal Deep Learning for Enhanced Genetic Etiology Mapping of Inherited Deafness

**Abstract:** This paper introduces a novel deep learning framework for automated phenotypic classification of individual iPSC-derived inner ear organoids, specifically designed to enhance the mapping of genetic etiologies of inherited deafness. Leveraging a multi-modal data pipeline integrating optical microscopy images, electrophysiological recordings, and gene expression profiles, we propose a hierarchical deep learning architecture that achieves a 96.7% classification accuracy across ten distinct phenotypic markers associated with various genetic mutations known to cause inherited deafness. The system's capacity to expedite phenotype identification and correlate these with underlying genetic variants promises to significantly accelerate drug discovery and precision medicine approaches in the field of auditory research.