REHEAR: Reverse Engineered Hearing Evaluation for Auditory-neuropathy Recognition

Descrizione del progetto

Una diagnosi accurata di un disturbo uditivo ancora poco conosciuto

Il disturbo dello spettro della neuropatia uditiva colpisce fino al 10% delle persone affette da perdita di udito. A differenza della perdita uditiva tipica, le persone che ne soffrono sono in grado di percepire i suoni avendo tuttavia difficoltà a comprendere il parlato, poiché il nervo uditivo trasmette i segnali con una temporizzazione alterata. Gli attuali strumenti diagnostici non sono in grado di identificare i meccanismi neurali specifici coinvolti nella patologia, rendendo imprevedibile la scelta del trattamento. Sostenuto dal programma di azioni Marie Skłodowska-Curie, il progetto REHEAR sta lavorando al primo quadro diagnostico computazionale per la neuropatia uditiva avvalendosi dell’ottimizzazione matematica e dell’intelligenza artificiale, con l’obiettivo di convertire i modelli di disfunzione neurale in test del linguaggio mirati che rendano rilevabili i problemi neurali sottostanti. L’approccio adottato sarà convalidato su soggetti normoudenti e su portatori di impianti cocleari, aprendo la strada a una diagnosi di precisione e a un trattamento migliorato.

Obiettivo

"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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