Predicting cochlear implant performance: Moving beyond single biomarkers and leveraging artificial intelligence
In: Open access government, Band 45, Heft 1, S. 60-63
ISSN: 2516-3817
Predicting cochlear implant performance: Moving beyond single biomarkers and leveraging artificial intelligence
Matthew Shew, Amit Walia, and Craig A. Buchman highlight that the significant variability in speech perception among cochlear implant users can be addressed by using a multi- faceted approach that incorporates emerging technologies like machine learning and artificial intelligence to improve outcome prediction models. Cochlear implants (CIs) represent one of the most successful neural prostheses, providing discrete frequency and intensity electrical stimulation to restore hearing, improve speech understanding, and enhance quality of life. Despite their transformative impact, a significant challenge remains: the substantial variability in speech perception outcomes among CI users. (1) While current methodologies excel at identifying individuals with severe hearing loss and, thus, strong CI candidates, they fall short in predicting the degree of benefit each recipient will gain from the device. This limitation hinders personalized care and underscores the need for more advanced predictive tools.