基于深度学习的人工智能算法来分类手绘螺旋的震动
Reghu Anandapadmanabhan1, Aayushi Vishnoi1, Geetha Raman2
1Department of Neurology, All India Institute of Medical Sciences (AIIMS), New Delhi, India.
概括
深度学习算法可以比人类专家更准确地从手绘的螺旋来分类综合征. 这项技术提供了一个客观的工具来诊断和分类各种震动条件.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 目前缺乏用于诊断和分类综合征的客观生物标志物.
- 的分类在很大程度上依赖于主观的临床评估.
- 开发客观的诊断工具对于有效的患者管理至关重要.
研究的目的:
- 开发和验证一种深度学习 (DL) 算法,用于使用手绘螺旋来分类震.
- 评估算法的性能与专家评级者对比.
- 提供一个客观的,独立于特征的震分类方法.
主要方法:
- 招募了患有各种震综合征的参与者 (静脉震,基本震,帕金森病,小脑动症) 和健康的志愿者.
- 使用手绘螺旋来通过转移学习训练DL算法 (InceptionResNetV2,Keras序列模型).
- 在独立队列上对该模型进行外部验证,将其准确性和F1分数与专家临床医生的分数进行比较.
主要成果:
- DL分类器最初的整体准确率为81%,在重新分析后的调整准确率为70%.
- 对1535个螺旋图的外部验证结果的准确率为61% (调整后为59%).
- DL算法显著超过了人类评分人员,他们获得了46%的准确性.
结论:
- 监督的DL算法可以有效地从简单的手绘螺旋来检测和分类震综合征.
- 这种方法提供了无偏见的,功能独立的分类,超过了人类评分器的性能.
- 基于DL的螺旋图的分析为震诊断提供了一个有希望的客观工具.
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