Machine learning algorithm uses brain scans to predict language ability in deaf children after they receive a cochlear implant.
In the new collaborative study between the Chinese University of Hong Kong and Ann & Robert H. Lurie Children’s Hospital of Chicago, researchers used artificial intelligence to understand brain structure underlying language development. The study has wide implications for children with developmental challenges. The work was published in the Proceedings of the National Academy of Sciences on January 15, 2018.
A cochlear implant is the most effective treatment for children born with hearing loss when hearing aids are not enough for the child to develop appropriate listening and language ability. Although a cochlear implant enables many children with hearing loss to understand and develop speech, some children lag behind their normal hearing peers despite receiving an implant as an infant or toddler.
The research work focuses on helping these children achieve the language and literacy of hearing, as these skills are critical to academic success, social, and emotional well-being.
“Since the brain underlies all human ability, the methods we have applied to children with hearing loss could have widespread use in predicting function and improving the lives of children with a broad range of disabilities,” said co-senior author Patrick C. M. Wong, PhD, a cognitive neuroscientist, professor and director of the Brain and Mind Institute at The Chinese University of Hong Kong.
According to Intraoperative Imaging Market report published by Coherent Market Insights, intraoperative imaging coupled with minimally invasive surgery provides real-time imaging, which helps surgeons to treat precise area of interest. Successful hearing and spoken language development depends on both the ear and the brain. Hearing loss early in life deprives the auditory areas of the brain of stimulation, which causes abnormal patterns in brain development. This novel approach reported by study could help in improving lifestyles of deaf children.