Forecasting Spoken Language Development in Children With Cochlear Implants Using Preimplant Magnetic Resonance Imaging
The results of this diagnostic study suggest that DTL prediction of language improvement on the individual child level using neuroanatomic features demonstrates greater accuracy, sensitivity, and specificity than traditional ML prediction. DTL was substantially improved by direct capture of discriminative and task-specific information that are advantages of representation learning enabled by this approach vs ML. The results support the feasibility of a single DTL prediction model for language prediction for children served by cochlear implant programs worldwide. Prediction of low improvement may enable targeted early and customized intervention to improve language.
Authors
- Maura E. Ryan (ORCID: https://orcid.org/0000-0002-7351-5895)
- Shani Dettman (ORCID: https://orcid.org/0000-0001-7373-7423)
- Nancy M. Young (ORCID: https://orcid.org/0000-0003-1498-2984)
- Di Yuan (ORCID: https://orcid.org/0000-0002-3570-2729)
- Dawn Choo (ORCID: https://orcid.org/0000-0002-0851-6410)
- Emily Shimeng Xu (ORCID: https://orcid.org/0000-0003-1662-1525)
- Yanlin Wang (ORCID: https://orcid.org/0009-0006-3607-7933)
- Denise Thomas
- Patrick C. M. Wong
Institutions
- Northwestern University (US)
- Lurie Children's Hospital (US)
- The University of Melbourne (AU)
- Chinese University of Hong Kong (HK)
- Lurie Children's Hospital (US)
Publication Details
- Journal
- JAMA Otolaryngology–Head & Neck Surgery
- Published
- 2025-12-26
- DOI
- https://doi.org/10.1001/jamaoto.2025.4694
- Primary Topic
- Hearing Loss and Rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 0.00