AI model using deep transfer learning – the most advanced form of machine learning – predicted with 92 % accuracy spoken language outcomes at one-to-three years after cochlear implants (implanted electronic hearing device), according to a large international study published in 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 U.S., who spoke three different languages (English, Spanish and Cantonese). The three centers in the study also used different protocols for scanning the brain and different outcome measures.
Such complex, heterogenous 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."
Nancy M. Young, Ann & Robert H. Lurie Children's Hospital of Chicago
This work was supported by the Research Grants Council of Hong Kong Grant GRF14605119, National Institutes of Health R21DC016069 and R01DC019387.
Dr. Young holds the Lillian S. Wells Professorship in Pediatric Otolaryngology at Lurie Children's. She also is Professor of Otolaryngology at Northwestern University Feinberg School of Medicine, and Professor and Fellow at Knowles Hearing Center, Department of Communication Sciences and Disorders at Northwestern University School of Communication.
Lurie Children's Cochlear Implant Program is one of the largest and most experienced in the world, with more than 2,000 cochlear implant procedures performed since its inception in 1991.
Source:
Journal reference:
Wang, Y., et al. (2025) Forecasting Spoken Language Development in Children With Cochlear Implants Using Preimplant Magnetic Resonance Imaging. JAMA Otolaryngology–Head & Neck Surgery. DOI:10.1001/jamaoto.2025.4694. https://jamanetwork.com/journals/jamaotolaryngology/fullarticle/2842669.