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[ARTICLE · art-134561] src=suny.technologypublisher.com ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

Smartphone-Based Objective Hearing Disorder Screening

The University at Buffalo has developed AudioSight, a smartphone-based platform that uses pupillometry, near-infrared hardware, and cloud-based AI to objectively screen for hearing disorders including hidden hearing loss, tinnitus, and hyperacusis. The prototype combines a 3D-printed smartphone attachment with a 15× macro microlens and concentric 730 nm near-infrared LEDs, estimated to cost less than $20 per unit in small-batch production, and runs a TemporalUNet pupil-segmentation model, XGBoost blink detection, and a BiLSTM-attention RiskNet model that generates risk scores from pupil-response features. The technology is at TRL 6 and is available for licensing or collaboration.

read2 min views17 publishedSep 17, 2026
Smartphone-Based Objective Hearing Disorder Screening
Image: Suny (auto-discovered)

| Smartphone-Based Objective Hearing Disorder Screening 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. 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. 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. Source: Max Pixel, CC0. Non-volitional measure of auditory function using the audio-evoked pupil response rather than relying only on self-report Low-cost smartphone-based hardware Designed for self-administration, and potentially suitable for children, older adults, cognitively impaired patients, and underserved populations At-home screening for hearing-related disorders Screening for hidden hearing loss, tinnitus, and hyperacusis Teleaudiology and remote patient monitoring Copyright for code and UI. TRL 6 Available for licensing or collaboration. Patent Information: | App Type | Country | Serial No. | Patent No. | Patent Status | File Date | Issued Date | Expire Date | |

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