"A one-size-fits-all intensive therapy approach is impractical and may not adequately address the needs of those children most at risk to fall behind," added Wong.

Erin Ingvalson, assistant professor at Florida State University who began work on the project as a post-doctoral student at Northwestern University, said "our goal is to eliminate the gap in language outcomes often found when children with hearing loss are compared to those with normal hearing."


"The ability to optimize therapy for each child with hearing loss will transform many lives," said Ingvalson.

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 of brain development.

"We used MRI to capture these abnormal patterns before cochlear implant surgery and constructed a machine-learning algorithm for predicting language development with a relatively high degree of accuracy, specificity and sensitivity," Wong explained. "Although the current algorithm is built for children with hearing impairment, research is being conducted to also predict language development in other pediatric populations."

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