A scoring system for cochlear implant candidate selection using artificial intelligence. (3rd April 2023)

• Record Type: Journal Article
• Authors: Abousetta, Alaa; El Kholy, Wafaa; Hegazy, Mona; Kolkaila, Enaas; Emara, Afaf; Serag, Shayma; Fathalla, Ahmed; Ismail, Omnia
• Is Part Of: Hearing, balance and communication. Volume 21:Number 2(2023)
• Page Start: 114
• Page End: 121
• Publication Date: 2023-04-03
• Subjects: Cochlear Implant -- artificial intelligence -- machine learning -- scoring -- candidate selection; Audiology -- Periodicals; Hearing disorders -- Periodicals; 612.85
• DOI: 10.1080/21695717.2023.2165371
• Languages: English
• ISSNs: 2169-5717

Abstract

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.