June 2016 Huffington Post and Trinity College Dublin
Intact hearing in early childhood is essential for normal development of communication skills and language. Neural circuits are responsible for the healthy development of hearing, which is foundational for most academic skills, such as reading and language communication. Deaf babies may suffer from an ongoing deficit in communication skills and language development if they pass the time window critical for the development of these neural circuits. So cochlear implantation at a very young age may be critical. However, there are some risks in a surgical procedure, hence it is useful to know which children will benefit and what age is ideal to have cochlear implantation.
Tan and colleagues used a statistical technique called machine learning to predict which children will gain the best language skills within 2 years of implantation. This technique, as applied to neuroimaging data, involves an algorithm that first has to “learn” how healthy brains activate during speech processing. Researchers feed several functional magnetic resonance imaging (fMRI) scans from the brains of normal hearing individuals into the algorithm. In the second stage, the algorithm creates a map of how the healthy brain functions based on what it learned. Finally, researchers feed a scan from the person they want to test into the algorithm.
In Tan’s study, brain scans were collected from typically developing children with intact hearing to use for training the algorithm. Hearing impaired children who were candidates for the cochlear implantation procedure, on average only 20 months old, were scanned before the implantation procedure. The algorithm was able to successfully discriminate the brain scans of the hearing impaired children from those of healthy children. These results are exciting because the modified machine learning approach implemented by the authors performed better than conventional approaches. Perhaps even more exciting is that the brain activation patterns in the hearing impaired children before cochlear implantation was predictive of their language performance two years after the surgery! Before using this method clinically, the results need to be replicated and validated. But, based on these present results, one thing is for sure: the future for these children sounds promising.
To obtain a high level of speech perception, the software for cochlear implants must be customised and adjusted for each individual child. This is a complex and time consuming rehabilitation programme managed by audiological scientists over numerous sessions in order to obtain optimal access to speech and environmental sounds for the patient. The National Centre for Cochlear Implants at Beaumont Hospital and IBM have launched a research project using cognitive computing to help predict speech perception in children with cochlear implants. It will improve device tuning and speed, but most critically it will improve overall patient outcomes in children with severe to profound hearing loss.
The project aims to use predictive modelling to help detect the subtle signs of vital changes in a patient's sound and speech perception to enable earlier proactive intervention. In addition, by analysing the diverse data from healthcare management systems, researchers can use machine based learning to aid the personalised approach to patient care and help to optimise the clinical decision-making processes while reducing costs.