Hearing impairment is a global health burden with nearly 20% of the global population living with some form of hearing loss. WHO data has shown that the economic impact of unaddressed hearing loss on society is greater than $980 billion annually! These costs relate, amongst others, to the loss of productivity and to societal costs as a result of social isolation, communication difficulties and stigma. Recent studies also suggest that hearing loss can increase the risk for dementia. Early diagnosis and early treatment are therefore crucial to reduce the impact that hearing loss has on society. The most common type of hearing loss is sensorineural hearing loss. Current standard hearing tests like the audiogram can detect this type of hearing loss when the hair cells in the inner ear are damaged. Until recently the received opinion was that sensorineural hearing loss occurs with the damage of these hair cells. However, in 2009, the discovery of a new type of hearing deficit that compromises the auditory-nerve synapses disrupted our present-day views on sensorineural hearing loss. This hearing deficit is called cochlear synaptopathy. Standard hearing tests like the audiogram are insensitive to cochlear synaptopathy, therefore this deficit is also called hidden hearing loss (HHL). HHL occurs several years before other cochlear structures (hair cells) are compromised. It is a general consequence of ageing and can additionally be caused by noise exposure or ototoxic treatment. HHL is often associated with speech intelligibility difficulties in noisy surroundings. Despite its presumed high prevalence, HHL is neither diagnosed nor treated in clinical practice.
The goal of EarDiTech is to develop precision hearing diagnostics and augmented hearing technologies to improve the lives of people with HHL. The EarDiTech project focuses on developing a robust, encephalogram-based diagnostic device (the CochSyn test device) to quantify HHL in humans. Based on this test, hearing-impaired auditory models are individualized to design hearing-aid signal processing that compensates for HHL. The model-based, augmented-hearing algorithm (the CoNNear algorithm) can offer an accessible treatment to those suffering from HHL and is based on a clever and versatile neural-network architecture that enables real-time sound processing.
Within this project, the diagnostic CochSyn test will first be implemented in a portable medical device and clinical trials will be performed with early-adopters and first-point-of-contact centres to demonstrate patient benefit and application range in a real-world clinical context. Secondly, hardware demonstrators that embed the CoNNear sound processing will be developed. They will be tailored for market-entry in the hearable, hearing-aid and cochlear-implant sectors.
With its innovative technology, EarDiTech goes beyond standard hearing care by addressing hearing loss from the start and providing high quality diagnostic and treatment opportunities for people suffering from hidden hearing loss.
