Nauman Hafeez;Nikolaos Boulgouris;Philip Begg;Richard Irving;Chris Coulson;Hao Wu;Huan Jia;Xinli Du
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引用次数: 0
摘要
作为最成功的人工耳蜗,人工耳蜗大大改善了许多人的听力,但听力效果却各不相同。电极阵列(EA)插入过程中的不确定性,包括创伤和深度控制,是其中一个因素。为了尽量减少 CT 扫描等成像方法的辐射,这项体外研究调查了双极电极阻抗和人工智能模型的使用情况,以确定 EA 插入深度。通过使用机器人馈线系统将商用 EA 插入按比例放大的二维鼓室模型,收集了复杂的阻抗数据。支持向量机模型对最终插入深度估计的分类准确率为 98%。CNN-LSTM 混合模型的 R 平方为 0.85,25 毫米插入过程中每毫米深度估计的平均绝对误差为 1.72 毫米。这种基于阻抗的深度评估方法可能有助于人工耳蜗植入手术,并可用于其他医疗植入应用。
Real-Time Insertion Depth Tracking of Cochlear Implant Electrode Array With Bipolar Complex Impedance and Machine Intelligence
Cochlear implants have significantly improved hearing for many as the most successful prosthesis, however, hearing outcomes vary. Uncertainty during electrode array (EA) insertion, including trauma and depth control, is one factor. To minimize radiation exposure from imaging methods like CT scans, this in-vitro study investigates the use of bipolar electrode impedance and artificial intelligent models to determine EA insertion depth. Complex impedance data was collected by inserting a commercial EA into a scaled-up 2D scala tympani model using a robotic feeder system. A support vector machine model produced a 98% classification accuracy for final insertion depth estimation. A CNN-LSTM hybrid model yielded 0.85 R-squared and 1.72 mm mean absolute error in depth estimation at each millimeter during a 25 mm insertion. This approach to depth assessment based on impedance may help with cochlear implant procedures and find use in other medical implant applications.