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Interpretability-driven deep learning for SERS-based classification of respiratory viruses
Hyunju Kang1, Junhyeong Lee2, Soo Hyun Lee3
1Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
A new diagnostic platform uses 3D plasmonic nanopillars and deep learning to rapidly detect multiple respiratory viruses, including SARS-CoV-2 variants, with over 98% accuracy. This technology offers a scalable, label-free solution for accurate, real-world diagnostics.
Area of Science:
- Nanotechnology
- Biomedical Engineering
- Machine Learning
Background:
- Respiratory viruses like influenza, RSV, and SARS-CoV-2 pose significant global health risks.
- Accurate and rapid variant-level diagnostics are crucial for managing outbreaks.
- Existing diagnostic methods may lack speed, accuracy, or the ability to differentiate specific variants.
Purpose of the Study:
- To develop an integrated diagnostic platform for the rapid detection and differentiation of multiple respiratory viruses.
- To leverage surface-enhanced Raman scattering (SERS) and 3D plasmonic nanopillars for enhanced viral detection.
- To apply interpretability-driven deep learning for accurate virus classification and model transparency.
Main Methods:
- Development of a diagnostic platform utilizing 3D plasmonic nanopillar substrates for enhanced SERS signal acquisition.
- Training a one-dimensional convolutional neural network (1D-CNN) on SERS spectra from 13 respiratory virus types, including SARS-CoV-2 variants.
- Application of gradient-weighted class activation mapping (Grad-CAM) to identify critical Raman shift regions for virus discrimination.
Main Results:
- Achieved over 98% classification accuracy in identifying 13 respiratory virus types using the 1D-CNN model.
- Demonstrated robust and reproducible capture of viral components, enhancing SERS signals for molecular fingerprinting.
- Validated reliable performance in complex clinical samples, confirming real-world applicability.
Conclusions:
- The developed platform provides a scalable, label-free solution for rapid, accurate, and variant-level respiratory virus detection.
- The integration of 3D SERS substrates and deep learning enhances diagnostic capabilities.
- The technology holds potential for point-of-care applications and improved epidemiological surveillance.
