Abstract：
OBJECTIVE: This study aimed to evaluate the differences in electrically evoked compound action potential (ECAP) thresholds and postoperative mapping current (T) levels between electrode types after cochlear implantation, the correlation between ECAP thresholds and T levels, and the performance of machine learning techniques in predicting postoperative T levels.
METHODS: Retrospective case review.
METHODS: Tertiary hospital.
METHODS: We reviewed the charts of 124 ears of children with severe-to-profound hearing loss who had undergone cochlear implantation.
METHODS: We compared ECAP thresholds and T levels from different electrodes, calculated correlations between ECAP thresholds and T levels, and created five prediction models of T levels at switch-on and 6 months after surgery.
METHODS: The accuracy of prediction in postoperative mapping current (T) levels.
RESULTS: The ECAP thresholds of the slim modiolar electrodes were significantly lower than those of the straight electrodes on the apical side. However, there was no significant difference in the neural response telemetry thresholds between the two electrodes on the basal side. Lasso regression achieved the most accurate prediction of T levels at switch-on, and the random forest algorithm achieved the most accurate prediction of T levels 6 months after surgery in this dataset.
CONCLUSIONS: Machine learning techniques could be useful for accurately predicting postoperative T levels after cochlear implantation in children.
METHODS: Retrospective case review.
METHODS: Tertiary hospital.
METHODS: We reviewed the charts of 124 ears of children with severe-to-profound hearing loss who had undergone cochlear implantation.
METHODS: We compared ECAP thresholds and T levels from different electrodes, calculated correlations between ECAP thresholds and T levels, and created five prediction models of T levels at switch-on and 6 months after surgery.
METHODS: The accuracy of prediction in postoperative mapping current (T) levels.
RESULTS: The ECAP thresholds of the slim modiolar electrodes were significantly lower than those of the straight electrodes on the apical side. However, there was no significant difference in the neural response telemetry thresholds between the two electrodes on the basal side. Lasso regression achieved the most accurate prediction of T levels at switch-on, and the random forest algorithm achieved the most accurate prediction of T levels 6 months after surgery in this dataset.
CONCLUSIONS: Machine learning techniques could be useful for accurately predicting postoperative T levels after cochlear implantation in children.
摘要：
目的：本研究旨在评估人工耳蜗植入后电极类型之间电诱发复合动作电位（ECAP）阈值和术后映射电流（T）水平的差异，ECAP阈值和T水平之间的相关性，以及机器学习技术在预测术后T水平方面的表现。
方法：回顾性病例回顾。
方法：三级医院。
方法：我们回顾了124只接受人工耳蜗植入的重度至重度听力损失儿童耳朵的图表。
方法：我们比较了来自不同电极的ECAP阈值和T水平，计算ECAP阈值和T水平之间的相关性，并创建了5个T水平的预测模型，在手术打开和6个月后。
方法：术后映射电流（T）水平的预测准确性。
结果：细长的modiolar电极的ECAP阈值显着低于顶端侧的直电极。然而,基底侧两个电极之间的神经反应遥测阈值没有显着差异。Lasso回归在开机时实现了对T水平的最准确预测，随机森林算法在该数据集中实现了对手术后6个月T水平的最准确预测。
结论：机器学习技术可用于准确预测儿童人工耳蜗植入术后T水平。
方法：回顾性病例回顾。
方法：三级医院。
方法：我们回顾了124只接受人工耳蜗植入的重度至重度听力损失儿童耳朵的图表。
方法：我们比较了来自不同电极的ECAP阈值和T水平，计算ECAP阈值和T水平之间的相关性，并创建了5个T水平的预测模型，在手术打开和6个月后。
方法：术后映射电流（T）水平的预测准确性。
结果：细长的modiolar电极的ECAP阈值显着低于顶端侧的直电极。然而,基底侧两个电极之间的神经反应遥测阈值没有显着差异。Lasso回归在开机时实现了对T水平的最准确预测，随机森林算法在该数据集中实现了对手术后6个月T水平的最准确预测。
结论：机器学习技术可用于准确预测儿童人工耳蜗植入术后T水平。
