基于富里埃变换红外光谱学和深度学习的类风湿性关节炎和结性脊柱炎的快速诊断
Photodiagnosis and photodynamic therapy
|November 6, 2023
概括
风湿性关节炎和结性脊柱炎的早期诊断是可以使用福里埃变换红外光谱 (FTIR) 和多尺度卷积神经网络 (MSCNN) 深度学习模型. 这种非侵入性方法在区分这些情况与健康对照时显示出高准确度.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 计算生物学 计算生物学
- 类风湿病学 类风湿病学
背景情况:
- 类风湿性关节炎和结性脊柱炎是使人虚弱的自身免疫性疾病,影响日常生活.
- 准确和早期诊断对于有效管理和预防残疾至关重要.
研究的目的:
- 开发和验证一种针对类风湿性关节炎和结性脊柱炎的非侵入性诊断方法.
- 为了利用富里埃变换红外光谱法 (FTIR) 与深度学习相结合,用于早期疾病检测.
主要方法:
- 收集了243个干燥血清样本 (每1个为结性脊髓炎,类风湿性关节炎和健康对照).
- 采用了一个多尺度卷积神经网络 (MSCNN) 模型,旨在融合本地特征以提高概括性.
- 集成的FTIR光谱与MSCNN模型进行分类.
主要成果:
- FTIR光谱分析显示了与生化成分相关的特定区域的明显差异.
- 该MSCNN模型实现了0.99的高诊断AUC和0.93.9的准确性.
- MSCNN显著超过了其他模型,如ANN,CNN和AlexNet.
结论:
- 多尺度卷积神经网络 (MSCNN) 能够有效地区分结性脊髓炎与类风湿性关节炎以及健康个体.
- FTIR光谱法为风湿性疾病的快速,敏感和非侵入性诊断提供了一个有前途的途径.
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