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Thus, outcome prediction is one of the most challenging topics in clinical cochlear implant-related research. For example, patients with the same inner ear disease and implanted with the same electrode array share a wide range of outcomes. Despite the many factors that may influence the outcome of cochlear implantation, less than 20% of the variability can be actually explained and nearly none of them can be targeted therapeutically. Many factors such as age, duration of deafness, genetics, variability in cochlear anatomy, surgical technique and device characteristics, neuronal survival, electrode position or general cognitive and central processing abilities may be attributed for the variation in speech perception. Indeed, a significant portion of the patients (up to 40%) experiences a less than expected hearing benefit with the device.

With this approach, the damaged sensory epithelium of the cochlea, which is responsible for translating sound into electrical signals, is bypassed.Īlthough it is the clinically most successful neuroprosthetic device, the high inter-individual variability of therapeutic success of the cochlear implant is one of the unanswered key questions. State of the art treatment of hearing loss is cochlear implantation, the insertion of an electrode array into the cochlea for direct electrical activation of the auditory nerve. Indeed, severe hearing loss affects more than 466 million people worldwide and this number is expected to rise dramatically within the next decades. When the inner ear is affected from disease, both organ functions, i.e., hearing and balance, rapidly deteriorate and in many cases do not recover.

ĭespite rapid technological improvement and implementation to the field, the human inner ear remains a black box. Specifically for organ systems, for which molecular knowledge is poor or if present was derived from animal models, computational biomedicine can give access to unexplored avenues in personalized medicine. Computational biomedicine integrating clinical and omics data is a modern and powerful approach for the development of novel, molecular and individualized diagnosis and treatment regimen.

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