用唾液红外光谱+深度学习，无创精准识别新冠感染者。
CNN-BiLSTM for sustainable and non-invasive COVID-19 detection via salivary ATR-FTIR spectroscopy
- 结合卷积与双向LSTM网络，处理唾液红外光谱数据。
- 在真实数据集上达80%准确率和F1分数，优于现有模型。
- 适合临床快速筛查，为无创检测提供新方案。
新冠疫情对全球医疗系统造成巨大压力，持续构成健康威胁，尤其面对新毒株的出现。尽管实时逆转录聚合酶链反应（RT-PCR）是新冠检测的金标准，但其成本高、耗时长、依赖人工且易受RNA提取影响。在此背景下，基于生物样本的衰减全反射傅里叶变换红外光谱（ATR-FTIR）分析提供了一种无需试剂、成本低的替代方法。本文提出一种新型CNN-BiLSTM架构，用于处理ATR-FTIR生成的光谱数据，实现从唾液样本中非侵入式诊断新冠感染。通过对比独立的CNN及其他先进机器学习方法，实验结果表明，该模型在具有挑战性的真实世界新冠数据集上平均准确率和F1分数达到0.80，显著优于其他模型。引入BiLSTM层后，模型性能明显提升，使CNN-BiLSTM成为利用唾液ATR-FTIR光谱检测新冠更准确、可靠的工具。
原文摘要 · Abstract (English)
The COVID-19 pandemic has placed unprecedented strain on healthcare systems and remains a global health concern, especially with the emergence of new variants. Although real-time polymerase chain reaction (RT-PCR) is considered the gold standard for COVID-19 detection, it is expensive, time-consuming, labor-intensive, and sensitive to issues with RNA extraction. In this context, ATR-FTIR spectroscopy analysis of biofluids offers a reagent-free, cost-effective alternative for COVID-19 detection. We propose a novel architecture that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks, referred to as CNN-BiLSTM, to process spectra generated by ATR-FTIR spectroscopy and diagnose COVID-19 from spectral samples. We compare the performance of this architecture against a standalone CNN and other state-of-the-art machine learning techniques. Experimental results demonstrate that our CNN-BiLSTM model outperforms all other models, achieving an average accuracy and F1-score of 0.80 on a challenging real-world COVID-19 dataset. The addition of the BiLSTM layer to the CNN architecture significantly enhances model performance, making CNN-BiLSTM a more accurate and reliable choice for detecting COVID-19 using ATR-FTIR spectra of non-invasive saliva samples.
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