用红外光谱和深度学习10分钟内精准筛查新冠感染者
On-Site Precise Screening of SARS-CoV-2 Systems Using a Channel-Wise Attention-Based PLS-1D-CNN Model with Limited Infrared Signatures
- 结合红外光谱与注意力机制的CNN模型，提升检测精度
- 在两个样本集上达96.48%准确率，满足世卫组织标准
- 适合疫情早期快速筛查，尤其样本量大时优势明显
在呼吸道病毒爆发初期，高效利用有限的鼻咽拭子样本进行快速准确筛查对公共健康至关重要。本研究提出一种方法，融合衰减全反射-傅里叶变换红外光谱（ATR-FTIR）与自适应迭代重加权惩罚最小二乘法（airPLS）预处理算法，以及基于通道注意力的偏最小二乘一维卷积神经网络（PLS-1D-CNN）模型，实现10分钟内精准筛查感染个体。在北京佑安医院采集了两组鼻咽拭子样本，分别包含126例和112例疑似奥密克戎变异株病例。由于ATR-FTIR光谱对实验条件敏感，影响信号质量，本文提出生物分子重要性（BMI）评估方法，通过对比BMI与PLS-GBM、PLS-RF结果验证其有效性。对于第二组样本（具有更高BMI值），采用airPLS进行预处理后，应用通道注意力型PLS-1D-CNN模型进行筛查。实验结果表明，该模型优于当前呼吸系统病毒光谱检测领域已有方法，识别准确率达96.48%，灵敏度96.24%，特异性97.14%，F1分数96.12%，AUC为0.99，满足世卫组织推荐标准：在中高通量场景下，敏感度≥95.00%，特异性≥97.00%。
原文摘要 · Abstract (English)
During the early stages of respiratory virus outbreaks, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the efficient utilize of limited nasopharyngeal swabs for rapid and accurate screening is crucial for public health. In this study, we present a methodology that integrates attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) with the adaptive iteratively reweighted penalized least squares (airPLS) preprocessing algorithm and a channel-wise attention-based partial least squares one-dimensional convolutional neural network (PLS-1D-CNN) model, enabling accurate screening of infected individuals within 10 minutes. Two cohorts of nasopharyngeal swab samples, comprising 126 and 112 samples from suspected SARS-CoV-2 Omicron variant cases, were collected at Beijing You'an Hospital for verification. Given that ATR-FTIR spectra are highly sensitive to variations in experimental conditions, which can affect their quality, we propose a biomolecular importance (BMI) evaluation method to assess signal quality across different conditions, validated by comparing BMI with PLS-GBM and PLS-RF results. For the ATR-FTIR signals in cohort 2, which exhibited a higher BMI, airPLS was utilized for signal preprocessing, followed by the application of the channel-wise attention-based PLS-1D-CNN model for screening. The experimental results demonstrate that our model outperforms recently reported methods in the field of respiratory virus spectrum detection, achieving a recognition screening accuracy of 96.48%, a sensitivity of 96.24%, a specificity of 97.14%, an F1-score of 96.12%, and an AUC of 0.99. It meets the World Health Organization (WHO) recommended criteria for an acceptable product: sensitivity of 95.00% or greater and specificity of 97.00% or greater for testing prior SARS-CoV-2 infection in moderate to high volume scenarios.
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