Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Abstract

Objective To develop a machine learning–based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline. Study Design Retrospective cohort. Setting Tertiary referral center. Patients 772 adults undergoing CICE from 2015 to 2020. Interventions Variables included demographics, unaided thresholds, and word recognition score. A random forest classification model was trained on patients undergoing CICE, and bootstrap cross-validation was used to assess the modeling approach's performance. Main Outcome Measures The machine learning–based referral tool was evaluated against the 60/60 guideline based on ability to identify CI candidates under traditional and expanded criteria. Results Of 587 patients with complete data, 563 (96%) met candidacy at our center, and the 60/60 guideline identified 512 (87%) patients. In the random forest model, word recognition score; thresholds at 3000, 2000, and 125; and age at CICE had the largest impact on candidacy (mean decrease in Gini coefficient, 2.83, 1.60, 1.20, 1.17, and 1.16, respectively). The 60/60 guideline had a sensitivity of 0.91, a specificity of 0.42, and an accuracy of 0.89 (95% confidence interval, 0.86–0.91). The random forest model obtained higher sensitivity (0.96), specificity (1.00), and accuracy (0.96; 95% confidence interval, 0.95–0.98). Across 1,000 bootstrapped iterations, the model yielded a median sensitivity of 0.92 (interquartile range [IQR], 0.85–0.98), specificity of 1.00 (IQR, 0.88–1.00), accuracy of 0.93 (IQR, 0.85–0.97), and area under the curve of 0.96 (IQR, 0.93–0.98). Conclusions A novel machine learning–based screening model is highly sensitive, specific, and accurate in predicting CI candidacy. Bootstrapping confirmed that this approach is potentially generalizable with consistent results.