AI model for predicting adult cochlear implant candidacy using routine behavioral audiometry

Objective: To describe an AI model to facilitate adult cochlear implant candidacy prediction based on basic demographical data and standard behavioral audiometry. Methods: A machine-learning approach using retrospective demographic and audiometric data to predict candidacy CNC word scores and AzBio sentence in quiet scores was performed at a tertiary academic center. Data for the model were derived from adults completing cochlear implant candidacy testing between January 2011 and March 2023. Comparison of the prediction model to other published prediction tools and benchmarks was performed. Results: The final dataset included 770 adults, encompassing 1045 AzBio entries, and 1373 CNC entries. Isophoneme scores and word recognition scores exhibited strongest importance to both the CNC and AzBio prediction models, followed by standard pure tone average and low-frequency pure tone average. The mean absolute difference between the predicted and actual score was 15 percentage points for AzBio sentences in quiet and 13 percentage points for CNC word scores, approximating anticipated test-retest constraints inherent to the variables incorporated into the model. Our final combined model achieved an accuracy of 87 % (sensitivity: 90 %; precision: 80 %). Conclusion: We present an adaptive AI model that predicts adult cochlear implant candidacy based on routine behavioral audiometric and basic demographical data. Implementation efforts include a public-facing online prediction tool and accompanying smartphone program, an embedded notification flag in the electronic medical record to alert providers of potential candidates, and a program to retrospectively engage past patients who may be eligible for cochlear implantation based on audiogram results.