{"slug": "smartphone-based-objective-hearing-disorder-screening", "title": "Smartphone-Based Objective Hearing Disorder Screening", "summary": "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.", "body_md": "|  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](https://en.wikipedia.org/wiki/Technology_readiness_level)  Available for licensing or collaboration.     **Patent Information:**  | App Type | Country | Serial No. | Patent No. | Patent Status | File Date | Issued Date | Expire Date |      |", "url": "https://wpnews.pro/news/smartphone-based-objective-hearing-disorder-screening", "canonical_source": "https://suny.technologypublisher.com/tech/Smartphone-Based_Objective_Hearing_Disorder_Screening", "published_at": "2026-09-17 07:16:24+00:00", "updated_at": "2026-09-19 13:53:26.909943+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research", "ai-products"], "entities": ["University at Buffalo", "AudioSight", "TemporalUNet", "XGBoost", "RiskNet", "BiLSTM"], "alternates": {"html": "https://wpnews.pro/news/smartphone-based-objective-hearing-disorder-screening", "markdown": "https://wpnews.pro/news/smartphone-based-objective-hearing-disorder-screening.md", "text": "https://wpnews.pro/news/smartphone-based-objective-hearing-disorder-screening.txt", "jsonld": "https://wpnews.pro/news/smartphone-based-objective-hearing-disorder-screening.jsonld"}}