通过使用深度学习的手指脉冲图像分析来实现超脂血的实时非侵入性检测
Hiruni Gunathilaka1,2, Rumesh Rajapaksha3, Thosini Kumarika3
1Department of Physics and Electronics, Faculty of Science, University of Kelaniya, Kelaniya, Sri Lanka.
Biomedical physics & engineering express
|November 19, 2025
概括
这项研究表明,深度学习可以通过手指脉冲波检测超脂血症,提供一种非侵入性查方法. 这种方法可以更早,更有效地识别心血管疾病风险.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心血管疾病的诊断 心血管疾病的诊断
背景情况:
- 超脂血症的检测是侵入性的和耗时的,对早期诊断构成挑战.
- 早期的超脂血症往往无症状,增加心血管疾病 (CVD) 的风险.
- 超脂血症的非侵入性诊断方法非常受欢迎.
研究的目的:
- 为了研究由高脂血症引起的脉冲波模式的可检测变化.
- 利用深度学习开发一种非侵入性,具有成本效益的超脂血症诊断方法.
- 评估定制卷积神经网络 (CNN) 和预训练模型用于超脂血症检测的有效性.
主要方法:
- 从81名高脂血症患者和65名对照人群中记录了脉冲波.
- 数据预处理和划分为培训,验证和测试集.
- 开发和评估定制的CNN架构,并对VGG16模型进行微调.
主要成果:
- 定制的CNN模型实现了高性能,准确度,精度,回忆率和F1得分约为95%-96%.
- VGG16模型表现出强的表现,其指标约为91%.
- 深度学习模型有效地区分了脉冲波,表明超脂血症改变了脉冲模式.
结论:
- 超脂血症导致脉冲波模式的可检测变化.
- 深度学习模型显示出非侵入性超脂血症查的巨大潜力.
- 这项研究可能会导致一种可靠,高效的设备,用于早期检测超脂血症和心血管疾病风险评估.
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