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Abstract
Objective: Cochlear implant (CI) candidate selection is a lengthy, complicated process that entails subjective judgment on the interaction of multiple pre-operative variables. It is assumed that setting a scoring system for the process of CI candidate selection would help in precise and reliable decision making. This would also provide a tool that would help in providing a better quality of life for CI patients.
Methods: Retrospective cohort study was held out in three post-CI rehabilitation centers. A total of 100 children records were analyzed with two statistical methods; conventional and Artificial Intelligence (AI) using Machine Learning. Language age deficit, phonological deficit, and social deficit were invented as new measures of CI performance; used to represent the developmental delay of those children in a single numeric value (in months).
Results: Artificial Intelligence analysis surpassed conventional statistical methods for the prediction of the outcome measures of post-CI performance. This was clearly expressed using linear regression models. The AI classification model validation for predictive accuracy of language age deficit, phonological deficit, and social deficit were 56.66%, 88.11%, and 40.46% respectively.
Conclusion: The production of a preliminary CI scoring model used for prediction of performance of patients was achieved. More data should be collected and fed to the software in order to improve its performance.
Acknowledgment
The authors report there are no competing interests to declare.
Disclosure statement
No potential conflict of interest was reported by the author(s).