REHEAR: Reverse Engineered Hearing Evaluation for Auditory-neuropathy Recognition

Descripción del proyecto

Diagnóstico preciso de un trastorno auditivo poco conocido

El trastorno del espectro de la neuropatía auditiva afecta hasta al 10 % de las personas con pérdida auditiva. A diferencia de la pérdida auditiva típica, las personas afectadas pueden percibir los sonidos, pero tienen dificultades para comprender el habla porque el nervio auditivo transmite las señales de forma desincronizada. Las herramientas diagnósticas actuales no permiten determinar los mecanismos neuronales concretos implicados, lo que dificulta la elección del tratamiento adecuado. En el proyecto REHEAR, respaldado por las acciones Marie Skłodowska-Curie, se busca desarrollar el primer marco de diagnóstico computacional para la neuropatía auditiva mediante el empleo de optimización matemática e inteligencia artificial. El objetivo es desarrollar pruebas del habla específicas a partir de los patrones de disfunción neuronal, de modo que permitan detectar los problemas neuronales subyacentes. El marco desarrollado se validará tanto en personas con audición normal como en personas con implantes cocleares, con el fin de mejorar la precisión del diagnóstico y la elección del tratamiento.

Objetivo

"Auditory Neuropathy Spectrum Disorder (ANSD) affects up to 10% of individuals with hearing loss. Unlike typical cases, people with ANSD detect sounds but struggle with speech understanding due to disrupted auditory nerve timing. Current tests cannot identify specific neural mechanisms, making treatment unpredictable. ANSD patients often cycle through multiple failed treatments - hearing aids provide minimal benefit, and cochlear implant outcomes vary dramatically from excellent to poor. This creates prolonged periods of communication difficulty during critical developmental years for children and significant quality-of-life impacts for adults. Cochlear implants provide unique access to neural responses via electrical recordings (ECAPs), enabling mechanism-specific diagnosis. This project develops the first computational-to-clinical bridge for ANSD using reverse engineering: instead of measuring neural dysfunction directly, we embed ANSD-specific disruption patterns into speech materials, creating diagnostic tests that make neural problems ""hearable."" Two complementary frameworks—mathematical optimization and AI-based learning—convert neural dysfunction patterns into targeted speech tests while preserving disorder-specific diagnostic signatures. The methodology includes three work packages: developing computational frameworks, validating through behavioral experiments with normal-hearing listeners (n=40) and cochlear implant users (n=12-15 each) alongside neural validation, and optimizing clinical protocols. Based at University Hospital Zurich's Deep Hearing Lab under Prof. Goehring's supervision, this fellowship advances expertise in computational neuroscience and clinical translation, delivering the first validated framework for ANSD subtype diagnosis and precision hearing care for thousands of patients annually across Europe, with potential healthcare savings through reduced diagnostic workup times and improved treatment matching.

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