AI model forecasts speech development in deaf children after cochlear implants

An AI model using deep transfer learning—the most advanced form of machine learning—has predicted spoken language outcomes with 92% accuracy from one to three years after patients received cochlear implants (implanted electronic hearing device).

The research is published in the journal JAMA Otolaryngology–Head & Neck Surgery.

Although cochlear implantation is the only effective treatment to improve hearing and enable spoken language for children with severe to profound hearing loss, spoken language development after early implantation is more variable in comparison to children born with typical hearing. If children who are likely to have more difficulty with spoken language are identified prior to implantation, intensified therapy can be offered earlier to improve their speech.

Researchers trained AI models to predict outcomes based on pre-implantation brain MRI scans from 278 children in Hong Kong, Australia and the U.S., who spoke three different languages (Cantonese, English and Spanish). The three centers in the study also used different protocols for scanning the brain and different outcome measures.

Such complex, heterogeneous datasets are problematic for traditional machine learning, but the study showed excellent results with the deep learning model. It outperformed traditional machine learning models in all outcome measures.

"Our results support the feasibility of a single AI model as a robust prognostic tool for language outcomes of children served by cochlear implant programs worldwide. This is an exciting advance for the field," said senior author Nancy M. Young, MD, Medical Director of Audiology and Cochlear Implant Programs at Ann & Robert H. Lurie Children's Hospital of Chicago—the U.S. center in the study.

"This AI-powered tool allows a 'predict-to-prescribe' approach to optimize language development by determining which child may benefit from more intensive therapy."