RaSizeNet: Un modelo interpretable de aprendizaje profundo multimodal que integra espectros SERS y distribución de...

Accurate and rapid identification of pathogens is essential for public health and infectious disease control. Despite notable advances, label-free, high-throughput, and sensitive bacterial detection in complex samples remains a significant challenge. An interpretable multimodal deep learning model, RaSizeNet, was developed by integrating surface-enhanced Raman spectroscopy (SERS) and inertial microfluidic size distribution for high-precision identification of complex bacterial populations. Fe@PCN-Au hybrid particles exhibiting strong SERS activity and magnetic responsiveness were synthesized and embedded into a dual-zone microfluidic chip to enable efficient bacterial enrichment, size-based separation, and signal enhancement. A large-scale multimodal dataset comprising nine representative bacterial species was constructed by combining SERS spectra and outlet distribution data. A size-guided cross-modal attention mechanism was introduced to improve spectral discriminability and model robustness while maintaining independent modality representations. RaSizeNet achieved an average accuracy of 96.22 % in complex samples, significantly outperforming unimodal (SERSNet, 82.23 %) and conventional multimodal (MultiNet, 92.81 %) models. The model also demonstrated superior adaptability and stability under extreme concentrations, varying flow rates, and diverse media conditions. This work establishes a promising framework for integrating microfluidic-SERS sensing with artificial intelligence, providing both theoretical and technical support for real-time multimodal pathogen detection.