Cochlear Implant
Cochlear implants are neural prosthetics restoring hearing to millions by electrically stimulating the auditory nerve. Current research focuses on improving implant performance through advanced signal processing techniques, including deep learning models like convolutional neural networks and recurrent neural networks, to enhance speech recognition and reduce distortion. This involves developing improved sound coding strategies and more accurate surgical planning using AI-driven image analysis of pre-operative scans and intraoperative microscopy, aiming to optimize electrode placement and minimize invasiveness. These advancements hold significant promise for improving the quality of life for individuals with hearing loss.
Papers
An Implantable Piezofilm Middle Ear Microphone: Performance in Human Cadaveric Temporal Bones
John Z. Zhang, Lukas Graf, Annesya Banerjee, Aaron Yeiser, Christopher I. McHugh, Ioannis Kymissis, Jeffrey H. Lang, Elizabeth S. Olson, Hideko Heidi Nakajima
The UmboMic: A PVDF Cantilever Microphone
Aaron J. Yeiser, Emma F. Wawrzynek, John Z. Zhang, Lukas Graf, Christopher I. McHugh, Ioannis Kymissis, Elizabeth S. Olson, Jeffrey H. Lang, Hideko Heidi Nakajima
Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve
Jacob de Nobel, Anna V. Kononova, Jeroen Briaire, Johan Frijns, Thomas Bäck
WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT
Xin Hua, Zhijiang Du, Hongjian Yu, Jixin Ma, Fanjun Zheng, Cheng Zhang, Qiaohui Lu, Hui Zhao