Researchers at the University at Buffalo have developed SYNAPSE (Synchronized Neural and Auditory Pupillometry for Sensory Evaluation), a multimodal assessment platform that objectively evaluates hearing-related disorders by combining synchronized EEG brain-activity recordings with pupil-response monitoring. The system uses around-the-ear electrodes, infrared eye tracking, and standardized auditory tests to measure responses linked to tinnitus, hyperacusis, misophonia, and related sound-tolerance disorders. By integrating physiological biomarkers rather than relying solely on patient self-reports, SYNAPSE may improve diagnostic accuracy and treatment monitoring. The patent-pending technology has been implemented in human participant studies, reached TRL 6 development, and is available for licensing or collaboration. SYNAPSE could expand access to more objective and reliable assessment of auditory disorders, particularly for individuals who have difficulty describing symptoms. Improved diagnosis and treatment monitoring may lead to better clinical care, enhanced patient outcomes, and new insights into the biological mechanisms underlying sound-sensitivity conditions.

Researchers have developed a novel diagnostic approach for Meniere’s disease that analyzes fatty acid and other molecular markers found in earwax using mass spectrometry. The technology offers a non-invasive method to identify biochemical signatures associated with the inner ear disorder, which is often difficult to diagnose because its symptoms overlap with those of other vestibular conditions. By providing an objective molecular assessment, the approach could improve diagnostic accuracy, enable earlier detection, and support monitoring of disease progression and treatment response. The patent-pending technology, currently at TRL 4, represents a promising advancement in otolaryngology and is available for licensing. Earlier and more reliable diagnosis of Meniere’s disease could reduce misdiagnoses, shorten the time to appropriate treatment, help preserve hearing, and improve quality of life for patients experiencing vertigo, tinnitus, and hearing loss.

Researchers at the University at Buffalo have developed AudioSight, a smartphone-based screening platform that objectively assesses hearing-related disorders using pupil responses to sound. The system combines a low-cost 3D-printed smartphone attachment, near-infrared illumination, and artificial intelligence to analyze audio-evoked pupillary responses. Advanced machine learning models process features such as response latency, dilation speed, and recovery time to generate risk scores for conditions including hidden hearing loss, tinnitus, and hyperacusis. Designed for self-administration and remote use, the technology could expand access to hearing screening for children, older adults, cognitively impaired individuals, and underserved populations. The TRL 6 technology is available for licensing or collaboration. AudioSight could make hearing-disorder screening more accessible, affordable, and objective by enabling at-home assessment and teleaudiology services. Earlier identification of hearing disorders may improve treatment outcomes, reduce barriers to care, and support populations that have difficulty completing conventional hearing tests.