选阻塞性睡眠呼吸暂停患者通过深度学习知识蒸在横向脑图
Min-Jung Kim1, Jiheon Jeong1,2, Jung-Wook Lee1
1Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-Gil Songpa-gu, Seoul, 05505, Republic of Korea.
Scientific reports
|October 18, 2023
概括
深度学习模型可以使用横向脑图识别阻塞性睡眠呼吸暂停 (OSA). 知识蒸有助于克服成像差异,提高这种常见睡眠障碍的诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种严重的疾病,通常是晚发现.
- 侧向脑图在正牙科中使用,但其用于OSA查的潜力尚未得到充分探索.
- 基于人工智能的诊断工具面临着一项挑战,即放射学模式的差异.
研究的目的:
- 开发一种深度学习模型,使用横向脑图来对阻塞性睡眠呼吸暂停 (OSA) 进行分类.
- 解决和克服不同放射成像设备模式的挑战.
- 为了提高横向脑图的临床实用性,用于OSA查.
主要方法:
- 提出了一个包含基于特征的知识蒸的深度学习模型.
- 在分类任务中使用ResNet-50架构.
- 该模型在500名OSA和498名非OSA患者的侧向脑图上进行了训练和评估,这些患者使用了两种不同的成像设备.
主要成果:
- 知识蒸模型显示了高分类性能,根据接收器运行特征曲线分析下的面积表示.
- 梯度加权类激活映射证实了该模型对模式诱导特征的稳定性.
- 该模型显示,在预测OSA概率时,克服模式差异的能力有所提高.
结论:
- 通过知识蒸进行深度学习,有效地将OSA从侧向脑图分类.
- 这种方法成功地减轻了不同放射成像设备所带来的挑战.
- 开发的模型显示了在OSA查中的实际临床应用的希望.
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