A mobile health platform that uses smartphone-based pupillometry, low-cost near-infrared hardware, and AI analysis to objectively screen for and track hearing-related disorders such as hidden hearing loss, tinnitus, and hyperacusis.
BACKGROUND:
Hearing disorders are widespread, but many current screening and diagnostic approaches depend on subjective patient responses or expensive clinic-based equipment. This can limit accessibility for children, older adults, cognitively impaired patients, rural populations, and individuals who cannot reliably complete conventional hearing tests.
TECHNOLOGY OVERVIEW:
University at Buffalo technology AudioSight combines a smartphone application, a custom 3D-printed smartphone attachment, near-infrared illumination, and cloud-based artificial intelligence analysis. The attachment includes a 15× macro microlens, concentric 730 nm near-infrared LEDs, and a soft eye cup designed to reduce ambient light and stabilize pupil imaging. The prototype hardware is estimated to cost less than $20 per unit in small-batch production.
The AI analysis pipeline uses a TemporalUNet model for pupil segmentation, XGBoost-based blink detection and artifact correction, and feature extraction from the pupil-response curve, including response latency, peak and valley amplitudes, dilation velocity, area under the curve, and recovery time. A BiLSTM-attention model called RiskNet combines these features with clinical information to generate risk scores for auditory dysfunctions including hidden hearing loss, tinnitus, and hyperacusis.
ADVANTAGES:
APPLICATIONS:
INTELLECTUAL PROPERTY SUMMARY: Copyright for code and UI.
STAGE OF DEVELOPMENT: TRL 6
LICENSING STATUS : Available for licensing or collaboration.
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