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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

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机构: [1]Capital Med Univ, Beijing Tongren Hosp, Dept Otorhinolaryngol Head & Neck Surg, Key Lab Otolaryngol Head & Neck Surg,Minist Educ, Beijing, Peoples R China [2]Beihang Univ, Sch Math Sci, Lab Haihui Data Anal, Beijing, Peoples R China [3]Capital Med Univ, Beijing Tongren Hosp, Beijing Inst Otolaryngol, Beijing, Peoples R China
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关键词: cochlear nerve deficiency cochlear implantation machine learning stability selection support vector machines

摘要:
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.

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出版当年[2021]版:
大类 | 3 区 医学
小类 | 3 区 神经科学
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 神经科学
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Q2 NEUROSCIENCES
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Q2 NEUROSCIENCES

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第一作者机构: [1]Capital Med Univ, Beijing Tongren Hosp, Dept Otorhinolaryngol Head & Neck Surg, Key Lab Otolaryngol Head & Neck Surg,Minist Educ, Beijing, Peoples R China
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