Hearing impairment represents a major and growing global health burden, with nearly 20% of the world’s population affected by some degree of hearing loss. According to the World Health Organization, the economic cost of unaddressed hearing loss exceeds $980 billion annually, driven not only by reduced productivity but also by broader societal impacts such as social isolation, communication difficulties, and stigma. Increasing evidence further links hearing loss to an elevated risk of cognitive decline and dementia, underscoring the importance of early diagnosis and timely intervention to mitigate its long-term consequences.
The most common form of hearing loss is sensorineural hearing loss, traditionally identified using standard clinical tests such as the audiogram, which detect damage to sensory hair cells in the inner ear. For decades, this hair-cell damage was considered the primary cause of sensorineural deficits. However, a paradigm shift occurred in 2009 with the discovery of cochlear synaptopathy—a previously unrecognized form of hearing impairment affecting the synapses between hair cells and auditory nerve fibers. This condition, often referred to as hidden hearing loss (HHL), is not detectable with conventional audiometric tests and can manifest years before measurable hair-cell damage occurs. HHL is commonly associated with ageing but can also result from noise exposure or ototoxic treatments, and it is strongly linked to difficulties in understanding speech in noisy environments. Despite its likely high prevalence, HHL remains largely undiagnosed and untreated in current clinical practice.
Against this backdrop, EarDiTech aims to transform hearing care through the development of precision diagnostics and advanced augmented-hearing solutions specifically targeting HHL. Central to this effort is the development of the CochSyn test device, an electroencephalography-based diagnostic tool designed to objectively quantify cochlear synaptopathy in humans. Building on these diagnostic insights, individualized auditory models are created to inform tailored hearing-aid signal processing strategies. This approach is implemented in the CoNNear algorithm, a flexible and efficient neural-network-based sound processing framework capable of real-time operation, offering a scalable and accessible treatment option for individuals affected by HHL.
Within the project, the CochSyn diagnostic was integrated into a portable medical device and validated through clinical trials involving early adopters and primary care settings, demonstrating both patient benefit and real-world applicability. In parallel, hardware demonstrators embedding the CoNNear processing algorithms were developed and optimized for deployment across hearables, hearing aids, and cochlear implant systems, paving the way for market entry. By addressing hearing loss at its earliest, previously undetectable stage, EarDiTech’s innovations go beyond the limitations of conventional hearing care, enabling earlier intervention and providing high-quality diagnostic and therapeutic solutions for individuals living with hidden hearing loss.
