Zhao Shuai, Xu Meimei, Lin Chenglong, Zhang Weida, Li Dan, Peng Yusi, Tanemura Masaki, Yang Yong
State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai 200050, China.
Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Biosensors (Basel). 2025 Jul 16;15(7):458. doi: 10.3390/bios15070458.
Highly infectious and pathogenic viruses seriously threaten global public health, underscoring the need for rapid and accurate diagnostic methods to effectively manage and control outbreaks. In this study, we developed a comprehensive Surface-Enhanced Raman Scattering-Lateral Flow Immunoassay (SERS-LFIA) detection system that integrates SERS scanning imaging with artificial intelligence (AI)-based result discrimination. This system was based on an ultra-sensitive SERS-LFIA strip with SiO-Au NSs as the immunoprobe (with a theoretical limit of detection (LOD) of 1.8 pg/mL). On this basis, a negative-positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. The proposed machine learning method significantly reduced the interference of abnormal signals and achieved reliable detection at concentrations as low as 2.5 pg/mL, which was close to the theoretical Raman LOD. The accuracy of the proposed ResNet-18 image recognition model was 100% for the training set and 94.52% for the testing set, respectively. In summary, the proposed SERS-LFIA detection system that integrates detection, scanning, imaging, and AI automated result determination can achieve the simplification of detection process, elimination of the need for specialized personnel, reduction in test time, and improvement of diagnostic reliability, which exhibits great clinical potential and offers a robust technical foundation for detecting other highly pathogenic viruses, providing a versatile and highly sensitive detection method adaptable for future pandemic prevention.
高传染性和致病性病毒严重威胁全球公共卫生，凸显了快速准确诊断方法对于有效管理和控制疫情爆发的必要性。在本研究中，我们开发了一种综合的表面增强拉曼散射-侧向流动免疫分析（SERS-LFIA）检测系统，该系统将SERS扫描成像与基于人工智能（AI）的结果判别相结合。该系统基于一种以SiO-Au纳米棒作为免疫探针的超灵敏SERS-LFIA试纸条（理论检测限（LOD）为1.8 pg/mL）。在此基础上，开发了一种将SERS扫描成像与深度学习模型（ResNet-18）相结合的阴性-阳性判别方法，以分析T线附近的探针分布模式。所提出的机器学习方法显著降低了异常信号的干扰，并在低至2.5 pg/mL的浓度下实现了可靠检测，这接近理论拉曼检测限。所提出的ResNet-18图像识别模型在训练集上的准确率为100%，在测试集上的准确率为94.52%。总之，所提出的集成检测、扫描、成像和AI自动结果判定的SERS-LFIA检测系统能够实现检测过程的简化、无需专业人员、减少测试时间并提高诊断可靠性，具有巨大的临床潜力，为检测其他高致病性病毒提供了坚实的技术基础，提供了一种适用于未来大流行预防的通用且高度灵敏的检测方法。
