利用基于通道注意力的PLS-1D-CNN模型和有限红外特征对SARS-CoV-2系统进行现场精准筛查
On-Site Precise Screening of SARS-CoV-2 Systems Using a Channel-Wise Attention-Based PLS-1D-CNN Model with Limited Infrared Signatures
AI总结：
该研究融合ATR-FTIR、airPLS与通道注意力PLS-1D-CNN，提出BMI信号质量评估，实现10分钟内SARS-CoV-2鼻咽拭子精准现场筛查。
摘要：
在严重急性呼吸综合征冠状病毒2（SARS-CoV-2）等呼吸道病毒暴发的早期阶段，高效利用有限的鼻咽拭子进行快速、准确的筛查对公共卫生至关重要。本研究提出一种将衰减全反射-傅里叶变换红外光谱（ATR-FTIR）、自适应迭代重加权惩罚最小二乘（airPLS）预处理算法与基于通道注意力的偏最小二乘一维卷积神经网络（PLS-1D-CNN）模型相结合的方法，可在10分钟内准确筛查感染者。研究在北京佑安医院采集了两批鼻咽拭子样本用于验证，分别包含来自SARS-CoV-2 Omicron变异株疑似病例的126份和112份样本。鉴于ATR-FTIR光谱对实验条件变化高度敏感，而这些变化会影响光谱质量，我们提出了一种生物分子重要性（BMI）评估方法，以评估不同条件下的信号质量，并通过将BMI与PLS-GBM和PLS-RF结果进行比较加以验证。对于表现出较高BMI的第2批队列ATR-FTIR信号，先使用airPLS进行信号预处理，随后应用基于通道注意力的PLS-1D-CNN模型进行筛查。实验结果表明，我们的模型优于呼吸道病毒光谱检测领域近期报道的方法，识别筛查准确率达到96.48%、灵敏度达到96.24%、特异度达到97.14%、F1分数达到96.12%，AUC达到0.99。其满足世界卫生组织（WHO）推荐的可接受产品标准：在中高通量场景下检测既往SARS-CoV-2感染时，灵敏度不低于95.00%，特异度不低于97.00%。
英文摘要：
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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