Cochlear Implantation in Postlingually Deaf Adults is Time-sensitive Towards Positive Outcome: Prediction using Advanced Machine Learning Techniques

Abstract

Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults. Moderate-to-moderately severe hearing loss can usually be corrected with hearing aids; however, severe-to-profound hearing loss often requires a cochlear implant (CI). However, post-operative CI results vary, and the performance of the previous prediction models is limited, indicating that a new approach is needed. For postlingually deaf adults (n de120) who received CI with full insertion, we predicted CI outcomes using a Random-Forest Regression (RFR) model and investigated the effect of preoperative factors on CI outcomes. Postoperative word recognition scores (WRS) served as the dependent variable to predict. Predictors included duration of deafness (DoD), age at CI operation (ageCI), duration of hearing-aid use (DoHA), preoperative hearing threshold and sentence recognition score. Prediction accuracy was evaluated using mean absolute error (MAE) and Pearson’s correlation coefficient r between the true WRS and predicted WRS. The fitting using a linear model resulted in prediction of WRS with r = 0.7 and MAE = 15.6 ± 9. RFR outperformed the linear model (r = 0.96, MAE = 6.1 ± 4.7, p < 0.00001). Cross-hospital data validation showed reliable performance using RFR (r = 0.91, MAE = 9.6 ± 5.2). The contribution of DoD to prediction was the highest (MAE increase when omitted: 14.8), followed by ageCI (8.9) and DoHA (7.5). After CI, patients with DoD < 10 years presented better WRSs and smaller variations (p < 0.01) than those with longer DoD. Better WRS was also explained by younger age at CI and longer-term DoHA. Machine learning demonstrated a robust prediction performance for CI outcomes in postlingually deaf adults across different institutes, providing a reference value for counseling patients considering CI. Health care providers should be aware that the patients with severe-to-profound hearing loss who cannot have benefit from hearing aids need to proceed with CI as soon as possible and should continue using hearing aids until after CI operation.

Cochlear Implantation in Candidates With Moderate‐to‐Severe Hearing Loss and Poor Speech Perception

“…The inclusion criteria aimed at patients with preoperative PTA ≤80 dB HL but with sufficiently poor speech comprehension for the use of a CI to be indicated; this can be regarded as a borderline condition for cochlear implantation. The inclusion criterion with regard to the preoperative PTA does not allow the determination of one outcome‐predicting factor in the established population of CI recipients, namely, the duration of severe‐to‐profound hearing loss 11–13 . Therefore, the secondary aim of the study was to derive a predictive model for this population in which the duration cannot be defined.…”

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

Cortical reorganization following auditory deprivation predicts cochlear implant performance in postlingually deaf adults

“…It is also noted that the use of hearing aid is associated with smaller volume in the left thalamus (Rudner et al, 2019). As smaller thalami and use of hearing aid (Kim et al, 2018b) are observed to associate with good CI outcomes, this may indicate that use of hearing aid modulates the thalamic cognitive function or helps keep the integrity of the original function and structure, possibly leading to a higher success rate of CI.…”

“…The limited prediction of CI outcomes when using the clinical features might be improved by also quantitatively evaluating brain MRI features, which account for the cross‐modal plasticity and the altered cortical structure and function that occurs in long‐term deafness. To analyze multivariate clinical and imaging features, nonlinear fitting algorithms such as a random forest regression model may be more suitable than previously used correlation approaches because sophisticated and multidimensional patterns of cortical structural changes are required to be taken into account (Kim et al, 2018a). Furthermore, modeling of a machine learning‐based regression using a training set and a validation set can build a computerized aiding system that allows for individualization of the prediction and may ultimately be able to assist clinicians in practice.…”