ArticleBrain and behavior2015
A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.
Article in Brain and behavior, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological psychiatry · 2023Pooled it
- Brain Morphological Modifications in Congenital and Acquired Auditory Deprivation: A Systematic Review and Coordinate-Based Meta-Analysis.Frontiers in neuroscience · 2022Pooled it
- Classification of Speech and Associated EEG Responses from Normal-Hearing and Cochlear Implant Talkers Using Support Vector Machines.Audiology research · 2025Article
- Personalizing Cochlear Implant Care in Single-Sided Deafness: A Distinct Paradigm from Bilateral Hearing Loss.Journal of personalized medicine · 2025Review
- A systematic review of machine learning approaches in cochlear implant outcomes.NPJ digital medicine · 2025Article
- Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review.Audiology research · 2025Review
- Perfecting Sensory Restoration and the Unmet Need for Personalized Medicine in Cochlear Implant Users: A Narrative Review.Brain sciences · 2025Review
- The neural characteristics influencing literacy outcome in children with cochlear implants.Brain communications · 2025Article
- Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Predicting synkinesis caused by Bell's palsy or Ramsay Hunt syndrome using machine learning-based logistic regression.Laryngoscope investigative otolaryngology · 2023Article
- Using Clinical Audiologic Measures to Determine Cochlear Implant Candidacy.Audiology & neuro-otology · 2022Article
- Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children.Frontiers in neuroscience · 2022Article
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.Clinical kidney journal · 2019Article
- Article
- Impedance Measures During in vitro Cochlear Implantation Predict Array Positioning.IEEE transactions on bio-medical engineering · 2018Article
- Neural preservation underlies speech improvement from auditory deprivation in young cochlear implant recipients.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume.Frontiers in computational neuroscience · 2017Article
- Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.Frontiers in computational neuroscience · 2016Article
- A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.Brain and behavior · 2015Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
introductionWe developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate.
methodsThe language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2). Based on the CELF-P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag-of-Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi-supervised models to classify CI users as effective or ineffective.
resultsCompared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi-supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave-one-out cross-validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset.
conclusionWe have validated the hypothesis that pre-implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi-supervised model over the supervised model. It is always worthwhile to try a semi-supervised model when unlabeled data are available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.