ArticleTrends in hearing
Predictive models for cochlear implant outcomes: Performance, generalizability, and the impact of cohort size.
Article in Trends in hearing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 47 citations in OpenAlex.
- Multi-Level Perception Systems in Fusion of Lifeforms: Classification, Challenges and Future Conceptions.Sensors (Basel, Switzerland) · 2026Review
- From Clinic to Reality: Integrating Sound Field Testing and Hearing Quality Measures in Cochlear Implant Users.Journal of clinical medicine · 2025Article
- Machine Learning Versus Simple Clinical Models for Cochlear Implant Outcome Prediction.Audiology research · 2025Article
- Integrating Computational Modelling into the Ecosystem of Cochlear Implantation: Advancing Access to Diagnostics, Decision-Making, and Post-Implantation Outcomes on a Global Scale.Journal of clinical medicine · 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
- External Validation and Extension of a Cochlear Implant Performance Prediction Model: Analysis of the Oldenburg Cohort.Audiology research · 2025Article
- A Machine Learning Model to Predict Postoperative Speech Recognition Outcomes in Cochlear Implant Recipients: Development, Validation, and Comparison with Expert Clinical Judgment.Journal of clinical medicine · 2025Article
- Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review.Audiology research · 2025Review
- Changes in participatory and societal outcomes during the waiting period for cochlear implantation - an observational study.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 · 2025Observational
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Using Machine Learning and Multifaceted Preoperative Measures to Predict Adult Cochlear Implant Outcomes: A Prospective Pilot Study.Ear and hearing · 2024Article
- Investigation of Maximum Monosyllabic Word Recognition as a Predictor of Speech Understanding with Cochlear Implant.Journal of clinical medicine · 2024Article
- Word Recognition with a Cochlear Implant in Relation to Prediction and Electrode Position.Journal of clinical medicine · 2023Article
- The Effects of Stimulus Repetition Rate on Electrically Evoked Auditory Brainstem Potentials in Postlingually Deafened Adult Cochlear Implant Recipients.Journal of clinical medicine · 2023Article
- Validation of a predictive model for speech discrimination after cochlear impIant provision.HNO · 2023Article
- [Validation of a predictive model for speech discrimination after cochlear implant provision].HNO · 2023Article
- Towards personalized and optimized fitting of cochlear implants.Frontiers in neuroscience · 2023Article
- Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation.Frontiers in computational neuroscience · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 6 institutions in 4 countries.
Funding
Abstract
While cochlear implants have helped hundreds of thousands of individuals, it remains difficult to predict the extent to which an individual's hearing will benefit from implantation. Several publications indicate that machine learning may improve predictive accuracy of cochlear implant outcomes compared to classical statistical methods. However, existing studies are limited in terms of model validation and evaluating factors like sample size on predictive performance. We conduct a thorough examination of machine learning approaches to predict word recognition scores (WRS) measured approximately 12 months after implantation in adults with post-lingual hearing loss. This is the largest retrospective study of cochlear implant outcomes to date, evaluating 2,489 cochlear implant recipients from three clinics. We demonstrate that while machine learning models significantly outperform linear models in prediction of WRS, their overall accuracy remains limited (mean absolute error: 17.9-21.8). The models are robust across clinical cohorts, with predictive error increasing by at most 16% when evaluated on a clinic excluded from the training set. We show that predictive improvement is unlikely to be improved by increasing sample size alone, with doubling of sample size estimated to only increasing performance by 3% on the combined dataset. Finally, we demonstrate how the current models could support clinical decision making, highlighting that subsets of individuals can be identified that have a 94% chance of improving WRS by at least 10% points after implantation, which is likely to be clinically meaningful. We discuss several implications of this analysis, focusing on the need to improve and standardize data collection.
Indexed as
Identifiers
What OpenQuestion holds
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.