用红外光谱和可解释模型10分钟内精准识别呼吸道病毒
Accurate Diagnosis of Respiratory Viruses Using an Explainable Machine Learning with Mid-Infrared Biomolecular Fingerprinting of Nasopharyngeal Secretions
- 结合红外光谱与新型可解释模型，快速分析鼻咽分泌物
- 在两个独立队列中灵敏度和特异性均超94.4%
- 能揭示病毒特异的分子指纹，适合临床诊断研发
准确识别呼吸道病毒（RVs）对疫情控制和公共卫生至关重要。本研究提出一种诊断系统，将鼻咽分泌物的衰减全反射傅里叶变换红外光谱（ATR-FTIR）与可解释的旋转位置嵌入稀疏注意力变压器（RoPE-SAT）模型结合，可在10分钟内精准识别多种呼吸道病毒。光谱数据采集范围为4000–400 cm⁻¹，分析使用生物指纹区（1800–900 cm⁻¹）。采用标准正态变量（SNV）归一化和二阶导数处理以减少散射和基线漂移。通过梯度加权类激活映射（Grad-CAM）生成显著性图，突出分类相关光谱区域，提升模型可解释性。在两家医院的两组独立队列中进行评估，分别使用不同病毒运送介质（VTMs）和干燥方法，一组包含流感B、SARS-CoV-2和健康对照，另一组包含支原体、SARS-CoV-2和健康对照。模型在两组队列中敏感度和特异性均超过94.40%。通过关联模型选择的红外区域与已知生物分子特征，验证了该系统能有效识别包括脂质、酰胺I/II/III、核酸和碳水化合物在内的病毒特异性光谱指纹，并利用其加权贡献实现精准分类。
原文摘要 · Abstract (English)
Accurate identification of respiratory viruses (RVs) is critical for outbreak control and public health. This study presents a diagnostic system that combines Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) from nasopharyngeal secretions with an explainable Rotary Position Embedding-Sparse Attention Transformer (RoPE-SAT) model to accurately identify multiple RVs within 10 minutes. Spectral data (4000-00 cm-1) were collected, and the bio-fingerprint region (1800-900 cm-1) was employed for analysis. Standard normal variate (SNV) normalization and second-order derivation were applied to reduce scattering and baseline drift. Gradient-weighted class activation mapping (Grad-CAM) was employed to generate saliency maps, highlighting spectral regions most relevant to classification and enhancing the interpretability of model outputs. Two independent cohorts from Beijing Youan Hospital, processed with different viral transport media (VTMs) and drying methods, were evaluated, with one including influenza B, SARS-CoV-2, and healthy controls, and the other including mycoplasma, SARS-CoV-2, and healthy controls. The model achieved sensitivity and specificity above 94.40% across both cohorts. By correlating model-selected infrared regions with known biomolecular signatures, we verified that the system effectively recognizes virus-specific spectral fingerprints, including lipids, Amide I, Amide II, Amide III, nucleic acids, and carbohydrates, and leverages their weighted contributions for accurate classification.
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