Real-Time Insertion Depth Tracking of Cochlear Implant Electrode Array With Bipolar Complex Impedance and Machine Intelligence

IF 3.4 Q2 ENGINEERING, BIOMEDICAL IEEE transactions on medical robotics and bionics Pub Date : 2024-03-30 DOI:10.1109/TMRB.2024.3407355
Nauman Hafeez;Nikolaos Boulgouris;Philip Begg;Richard Irving;Chris Coulson;Hao Wu;Huan Jia;Xinli Du
{"title":"Real-Time Insertion Depth Tracking of Cochlear Implant Electrode Array With Bipolar Complex Impedance and Machine Intelligence","authors":"Nauman Hafeez;Nikolaos Boulgouris;Philip Begg;Richard Irving;Chris Coulson;Hao Wu;Huan Jia;Xinli Du","doi":"10.1109/TMRB.2024.3407355","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":"6 3","pages":"1245-1255"},"PeriodicalIF":3.4000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on medical robotics and bionics","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10542359/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用双极复合阻抗和机器智能实时跟踪人工耳蜗电极阵列的插入深度
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.80
自引率
0.00%
发文量
0
期刊最新文献
Table of Contents IEEE Transactions on Medical Robotics and Bionics Society Information Guest Editorial Special section on the Hamlyn Symposium 2023—Immersive Tech: The Future of Medicine IEEE Transactions on Medical Robotics and Bionics Publication Information IEEE Transactions on Medical Robotics and Bionics Information for Authors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1