Aprendizado de redes neurais profundas para diagnóstico molecular rápido e não invasivo de COVID-19 pela saliva

Abstract

The COVID-19 pandemic exposed limitations of conventional diagnostic methods, such as RT-PCR, which, despite their high sensitivity, are costly, time-consuming, and logistically complex. Vibrational spectroscopy emerges as a promising alternative, offering rapid, low-cost, and reagent-free analyses. However, the complexity of biochemical changes reflected in Raman and ATR-FTIR spectra demands advanced analytical methods that surpass traditional chemometric approaches in extracting nonlinear patterns. This work develops and evaluates models based on deep learning, specifically Convolutional Neural Network (CNN) architectures and Convolutional Neural Networks integrated with Long Short-Term Memory (CNN-BiLSTM), applied to the analysis of non-invasive biological sample spectra for COVID-19 detection. CNNs offer superior capability for extracting local spatial features from spectra through convolution operations, while BiLSTM networks complement this analysis by capturing bidirectional temporal dependencies in sequential spectral data, enabling a more comprehensive understanding of complex spectral patterns. Two architectures were developed: CNN-Spectra was designed to analyze Raman spectra from blood serum, achieving 96.8% accuracy; CNN-BiLSTM-Spectra was developed to analyze ATR-FTIR spectra from saliva, reaching an average accuracy of 80% and outperforming traditional and state-of-the-art models. The results confirm the effectiveness of the proposed models in extracting relevant features from complex spectral data, improving classification between positive and negative COVID-19 samples. It is concluded that the integration of deep learning with vibrational spectroscopy is a promising approach for biomedical diagnosis, contributing to faster, more accurate, and sustainable detection methods.