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对于耳诊断的人工智能方法:利用高频听力测量数据来增强临床预测

Seyed-Ali Sadegh-Zadeh¹, Alireza Soleimani Mamalo², Kaveh Kavianpour³

¹Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom.

Frontiers in artificial intelligence

|May 22, 2024

概括

机器学习,特别是物流回归 (LR) 和人工神经网络 (ANN),使用高频音频计准确诊断耳声. 这种方法提供了一个比传统的主观评估更客观的工具来检测耳声.

科学领域:

听力学听力学是指听力学.
机器学习机器学习
医学诊断医学诊断医学诊断

背景情况:

耳的诊断往往依赖于患者的主观报告,导致潜在的不准确性.
高频听力测量数据提供了客观的测量方法,但在标准的耳评估中未得到充分利用.
机器学习 (ML) 提供了一个提高诊断客观性和准确性的机会.

研究的目的:

评估ML模型的有效性,包括后勤回归 (LR) 和人工神经网络 (ANN),用于耳诊断.
将ML模型性能与使用高频音频计数据的基线分类器进行比较.
建立一个更客观和可量化的声检测方法.

主要方法:

数据预处理和特征提取从高频音频计.
开发和评估LR,ANN和基线ML分类器.
使用精度,ROC曲线下的面积 (AUC),精度,回忆和F1分数进行性能评估.

主要成果:

由ANN支持的LR模型实现了94.06%的精度和97.06%的AUC.
ML模型显著优于传统的主观诊断方法.
高精度和回忆得分表明了强大的分类性能.

结论:

ML模型,特别是LR和ANN,提供了一种高度有效和客观的 tinnitus 诊断方法.
这种方法提升了早期和准确的耳声检测,可能改善患者的治疗结果.
这些发现支持将ML整合到临床声学中,以改善声管理.

关键词:

先进的审计数据分析,审计数据分析.高频听力测量仪的使用听力学中的机器学习客观的耳声评估在听力测量中的点检测.耳的诊断耳的诊断

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