Prediction of the Functional Status of the Cochlear Nerve in Individual Cochlear Implant Users Using Machine Learning and Electrophysiological Measures.
Prediction of the Functional Status of the Cochlear Nerve in Individual Cochlear Implant Users Using Machine Learning and Electrophysiological Measures.
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DOI：
10.1097/aud.0000000000000916
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
He S
中科院分区：
文献类型：
--
作者：
Skidmore J;Xu L;Chao X;Riggs WJ;Pellittieri A;Vaughan C;Ning X;Wang R;Luo J;He S
本研究旨在创建一个客观的预测模型，用于评估个体人工耳蜗 (CI) 用户的耳蜗神经 (CN) 功能状态。研究参与者包括 23 名患有耳蜗神经缺陷 (CND) 的儿童、29 名患有正常大小的耳蜗神经缺陷 (NSCN) 的儿童以及 20 名患有各种听力损失病因的成人。八名参与者是双边 CI 用户，并在双耳中进行了测试。结果，本研究共测试了 80 只耳朵。所有参与者都在测试耳中使用了 Cochlear® Nucleus™ CI。对于每个参与者，使用电极阵列上三个电极位点的电诱发复合动作电位 (eCAP) 的电生理测量来测量 CN 不应恢复功能 (RRF) 和输入/输出 (I/O) 功能。使用具有指数衰减函数的统计模型来估计耐火恢复时间常数。 I/O 函数的斜率使用线性回归进行估计。在预测模型中用作输入变量的 eCAP 参数是根据 RRF、eCAP 阈值、eCAP I/O 函数的斜率和负峰值（即 N1）延迟估计的绝对不应恢复时间。预测模型的输出变量是CN指数，是CN功能状态的指标。通过使用 CND 儿童和 NSCN 儿童的 eCAP 参数执行线性回归、支持向量机回归和逻辑回归来创建预测模型。采用图基诚实显着性差异标准的事后分析的单向方差分析用于比较研究组之间的研究变量。除一名患有 CND 的儿童外，所有儿童的 CN 指数均小于患有 NSCN 的儿童。成人 CI 用户测量的 CN 指数与 NSCN 儿童测量的 CN 指数没有显着差异。使用不同的机器学习技术计算时，观察到成年 CI 用户的 CN 指数存在差异。不管这些变化如何，在成年 CI 用户中使用所有三种技术计算出的 CN 指数与安静时测量的辅音-核心-辅音单词和 AzBio 句子分数显着相关。个人 CI 用户的 CN 功能状态是通过我们新开发的分析模型来估计的。成人 CI 用户个体 CN 功能的模型预测与语音感知表现呈显着正相关。本研究中提出的模型可能有助于理解和/或预测个体患者的 CI 结果。
This study aimed to create an objective predictive model for assessing the functional status of the cochlear nerve (CN) in individual cochlear implant (CI) users. Study participants included 23 children with cochlear nerve deficiency (CND), 29 children with normal-sized CNs (NSCNs), and 20 adults with various etiologies of hearing loss. Eight participants were bilateral CI users and were tested in both ears. As a result, a total of 80 ears were tested in this study. All participants used Cochlear® Nucleus™ CIs in their test ears. For each participant, the CN refractory recovery function (RRF) and input/output (I/O) function were measured using electrophysiological measures of the electrically-evoked compound action potential (eCAP) at three electrode sites across the electrode array. Refractory recovery time constants were estimated using statistical modeling with an exponential decay function. Slopes of I/O functions were estimated using linear regression. The eCAP parameters used as input variables in the predictive model were absolute refractory recovery time estimated based on the RRF, eCAP threshold, slope of the eCAP I/O function, and negative-peak (i.e., N1) latency. The output variable of the predictive model was CN index, an indicator for the functional status of the CN. Predictive models were created by performing linear regression, support vector machine regression, and logistic regression with eCAP parameters from children with CND and the children with NSCNs. One-way analysis of variance with post hoc analysis with Tukey’s honest significant difference criterion was used to compare study variables among study groups. All except for one child with CND had smaller CN indices than children with NSCNs. CN indices measured in adult CI users were not significantly different from those measured in children with NSCNs. Variations in CN index when calculated using different machine learning techniques were observed for adult CI users. Regardless of these variations, CN indices calculated using all three techniques in adult CI users were significantly correlated with Consonant-Nucleus-Consonant word and AzBio sentence scores measured in quiet. The functional status of the CN for individual CI users was estimated by our newly developed analytical models. Model predictions of CN function for individual adult CI users were positively and significantly correlated with speech perception performance. The models presented in this study may be useful for understanding and/or predicting CI outcomes for individual patients.