机器学习模型在ABR波形解读中的应用研究进展

Application of machine learning model in ABR waveform interpretation

豆慢慢;关静;王秋菊

1:解放军总医院第六医学中心耳鼻咽喉头颈外科医学部耳鼻咽喉内科国家耳鼻咽喉疾病临床医学研究中心

2:浙江中医药大学医学技术与信息工程学院

摘要
听性脑干反应(auditory brainstem respinse, ABR)是一项评估患者听力的客观的电生理测试,对其结果的解读主要是波I到波V潜伏期以及反应阈值的识别。波I到波V的潜伏期对临床医生判断患者听觉神经通路状况有重要的参考价值,因此这项检查结果的准确解读尤为重要,但目前ABR结果的分析完全依赖于听力师的人眼识别和解读,经验不足的听力师对其波形解读存在一定困难。为了解决这个问题,许多学者提出使用机器学习(machine learning, ML)模型来客观解读ABR结果,机器学习有着强大的学习能力,可以独立完成一份客观且准确的ABR结果解读,它为ABR结果的客观准确解读带来了曙光。
关键词
ABR波形;ABR分类;机器学习
基金项目(Foundation):
国家自然科学基金面上项目(82271171、82271189、82171130);国家自然科学基金重点项目(81830028);国家自然科学基金优秀青年基金项目(82222016);; 解放军总医院优青培育专项(2020-YQPY-004);; 军队医学科技青年培育计划孵化项目(19QNT058、21QNPY100)联合资助
作者
豆慢慢;关静;王秋菊
参考文献

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豆慢慢关静王秋菊