The Challenge: Unpredictable Outcomes

Cochlear implants have restored hearing for over one million individuals worldwide, yet outcomes remain highly variable. Some patients adapt rapidly, while others struggle despite optimal surgical and device factors. This reflects a fundamental issue: cochlear implantation is not only a device problem - it is a problem of neural adaptation.

A New Approach: Measuring Adaptation Early

As part of the UCSF Chen Scholars program, this work focuses on identifying early biomarkers of successful adaptation. We integrate behavioral measures such as pitch alignment and sound quality perception with neural signals including EEG-based cortical connectivity. These measures provide insight into how the brain integrates acoustic and electrical hearing within the first months after activation.

From Prediction to Personalization

The goal is not only to predict outcomes, but to intervene early. By identifying patients at risk for poor adaptation, we can tailor rehabilitation strategies, optimize device programming, and improve patient counseling.

This enables a shift toward personalized, data-driven cochlear implant care.

The Role of AI

Machine learning models integrate behavioral, neural, and clinical data to predict patient trajectories and identify patterns not captured by traditional metrics.

Looking Ahead

This work reflects a broader vision: integrating neuroscience, clinical care, and AI to transform how we evaluate and optimize outcomes in hearing restoration.