Summary
Cochlear synaptopathy (CS) is a recently discovered type of sensorineural hearing loss (SNHL) that compromises the integrity of the auditory-nerve population after ageing, noise-exposure or ototoxicity. This SNHL type occurs before the gold-standard clinical audiogram detects hearing problems and has functional consequences for neural sound encoding and communication in noisy environments. Despite its presumed high prevalence, CS is neither diagnosed nor treated in clinical practice. There is a WHO-identified need for early-diagnosis and treatment of SNHL to reduce the societal and economic burden of hearing loss, and CS-diagnosis falls within this category. In our ERC StG and PoC projects, we developed a robust, encephalogram-based diagnostic test to quantify CS in humans. Based on this test, we individualize hearing-impaired auditory models to design hearing-aid signal processing that compensates for CS. Our model-based, augmented-hearing algorithms can offer an accessible treatment to those suffering from CS and are based on a clever and versatile neural-network architecture that enables real-time sound processing.
In this project, we plan to implement our diagnostic CS-test (TRL4) in a portable medical device and perform clinical trials with early-adopters and first-point-of-contact centers to demonstrate its patient benefit and application range in a real-world clinical context (TRL5-6). Secondly, we aim to develop hardware demonstrators that embed our augmented-hearing sound processors. These real-time FPGA processors (TRL5) will be tailored for market-entry in the hearable, hearing-aid and cochlear-implant sectors. Along with consolidating our IP portfolio and setting out a business strategy, this challenge on Medical Technology and Devices will enable us to transition our proof-of-concept research discoveries to market with this project: EarDiTech: Precision Hearing Diagnostics and Augmented-hearing Technologies.
In this project, we plan to implement our diagnostic CS-test (TRL4) in a portable medical device and perform clinical trials with early-adopters and first-point-of-contact centers to demonstrate its patient benefit and application range in a real-world clinical context (TRL5-6). Secondly, we aim to develop hardware demonstrators that embed our augmented-hearing sound processors. These real-time FPGA processors (TRL5) will be tailored for market-entry in the hearable, hearing-aid and cochlear-implant sectors. Along with consolidating our IP portfolio and setting out a business strategy, this challenge on Medical Technology and Devices will enable us to transition our proof-of-concept research discoveries to market with this project: EarDiTech: Precision Hearing Diagnostics and Augmented-hearing Technologies.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101058278 |
| Start date: | 01-08-2022 |
| End date: | 31-07-2025 |
| Total budget - Public funding: | 2 499 416,00 Euro - 2 499 416,00 Euro |
Cordis data
Original description
Cochlear synaptopathy (CS) is a recently discovered type of sensorineural hearing loss (SNHL) that compromises the integrity of the auditory-nerve population after ageing, noise-exposure or ototoxicity. This SNHL type occurs before the gold-standard clinical audiogram detects hearing problems and has functional consequences for neural sound encoding and communication in noisy environments. Despite its presumed high prevalence, CS is neither diagnosed nor treated in clinical practice. There is a WHO-identified need for early-diagnosis and treatment of SNHL to reduce the societal and economic burden of hearing loss, and CS-diagnosis falls within this category. In our ERC StG and PoC projects, we developed a robust, encephalogram-based diagnostic test to quantify CS in humans. Based on this test, we individualize hearing-impaired auditory models to design hearing-aid signal processing that compensates for CS. Our model-based, augmented-hearing algorithms can offer an accessible treatment to those suffering from CS and are based on a clever and versatile neural-network architecture that enables real-time sound processing.In this project, we plan to implement our diagnostic CS-test (TRL4) in a portable medical device and perform clinical trials with early-adopters and first-point-of-contact centers to demonstrate its patient benefit and application range in a real-world clinical context (TRL5-6). Secondly, we aim to develop hardware demonstrators that embed our augmented-hearing sound processors. These real-time FPGA processors (TRL5) will be tailored for market-entry in the hearable, hearing-aid and cochlear-implant sectors. Along with consolidating our IP portfolio and setting out a business strategy, this challenge on Medical Technology and Devices will enable us to transition our proof-of-concept research discoveries to market with this project: EarDiTech: Precision Hearing Diagnostics and Augmented-hearing Technologies.
Status
SIGNEDCall topic
HORIZON-EIC-2021-TRANSITIONCHALLENGES-01-01Update Date
09-02-2023
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
