Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children

Simeng Lu; Jin Xie; Xingmei Wei; Ying Kong; Biao Chen; Jingyuan Chen; Lifang Zhang; Mengge Yang; Shujin Xue; Ying Shi; Sha Liu; Tianqiu Xu; Ruijuan Dong; Xueqing Chen; Yongxin Li; Haihui Wang

doi:10.3389/fnins.2022.895560

Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children

Front Neurosci. 2022 Jun 23:16:895560. doi: 10.3389/fnins.2022.895560. eCollection 2022.

Authors

Simeng Lu¹, Jin Xie², Xingmei Wei¹, Ying Kong³, Biao Chen¹, Jingyuan Chen¹, Lifang Zhang¹, Mengge Yang¹, Shujin Xue¹, Ying Shi¹, Sha Liu³, Tianqiu Xu³, Ruijuan Dong³, Xueqing Chen³, Yongxin Li¹, Haihui Wang²

Affiliations

¹ Key Laboratory of Otolaryngology Head and Neck Surgery, Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Ministry of Education, Capital Medical University, Beijing, China.
² Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, Beijing, China.
³ Beijing Tongren Hospital, Beijing Institute of Otolaryngology, Capital Medical University, Beijing, China.

Abstract

Cochlear nerve deficiency (CND) is often associated with variable outcomes of cochlear implantation (CI). We assessed previous investigations aiming to identify the main factors that determine CI outcomes, which would enable us to develop predictive models. Seventy patients with CND and normal cochlea who underwent CI surgery were retrospectively examined. First, using a data-driven approach, we collected demographic information, radiographic measurements, audiological findings, and audition and speech assessments. Next, CI outcomes were evaluated based on the scores obtained after 2 years of CI from the Categories of Auditory Performance index, Speech Intelligibility Rating, Infant/Toddler Meaningful Auditory Integration Scale or Meaningful Auditory Integration Scale, and Meaningful Use of Speech Scale. Then, we measured and averaged the audiological and radiographic characteristics of the patients to form feature vectors, adopting a multivariate feature selection method, called stability selection, to select the features that were consistent within a certain range of model parameters. Stability selection analysis identified two out of six characteristics, namely the vestibulocochlear nerve (VCN) area and the number of nerve bundles, which played an important role in predicting the hearing and speech rehabilitation results of CND patients. Finally, we used a parameter-optimized support vector machine (SVM) as a classifier to study the postoperative hearing and speech rehabilitation of the patients. For hearing rehabilitation, the accuracy rate was 71% for both the SVM classification and the area under the curve (AUC), whereas for speech rehabilitation, the accuracy rate for SVM classification and AUC was 93% and 94%, respectively. Our results identified that a greater number of nerve bundles and a larger VCN area were associated with better CI outcomes. The number of nerve bundles and VCN area can predict CI outcomes in patients with CND. These findings can help surgeons in selecting the side for CI and provide reasonable expectations for the outcomes of CI surgery.

Keywords: cochlear implantation; cochlear nerve deficiency; machine learning; stability selection; support vector machines.