Binary to Personalized: A Novel Machine Learning Probability Score for Cochlear Implant Candidacy

To develop and validate a machine learning (ML)-based screening tool for cochlear implant (CI) candidacy that generates personalized probability scores and supports individualized, patient-centered counseling.

Institutional CI registry retrospective cohort study and HERMES database (1878 patients, 3756 ears). Supervised ML classification models predicted CI candidacy: (1) ear-specific aided CNC word score < 50%, and (2) bilateral best-aided AzBio sentence scores < 60%. Three ML models (logistic regression, random forest, XGBoost) were benchmarked against the 60/60 rule (PTA ≥ 60 dB; WRS ≤ 60%) per TRIPOD-AI guidelines. Input features included audiometric thresholds, word recognition scores, age. Data were split 80/20, hyperparameter tuning via nested 10-fold cross-validation. Performance metrics included sensitivity, specificity, AUROC, F1 score, and calibration curves. SMOTE was applied to evaluate class imbalance effects. A web-based platform was deployed for user-friendly access.

For CNC < 50%, the 60/60 rule achieved a higher F1 score (0.83 vs. 0.77-0.79), but ML models demonstrated higher sensitivity (0.80-0.84 vs. 0.77) and excellent AUROC (0.88-0.89). For AzBio < 60%, ML models outperformed the 60/60 rule in F1 score (0.79-0.80 vs. 0.71), precision (0.77-0.78 vs. 0.68), and specificity (0.86-0.87 vs. 0.76), with AUROC 0.89-0.90. Calibration analysis confirmed well-calibrated, clinically interpretable probability distributions. SMOTE-balanced datasets did not alter performance.

Although ML models are not dramatically superior to the 60/60 rule, they offer complementary value by identifying candidates who might otherwise be missed and providing individualized probability scores for both CNC and AzBio CI candidacy criteria. These outputs enable more personalized counseling and shared decision-making, particularly for patients in the borderline "gray zone".