Explainable Deep Learning Framework for SERS Bio-quantification
This work addresses the need for explainable and accurate bio-quantification in SERS for medical diagnostics, though it is incremental as it builds on existing deep learning methods with a new explainability approach.
The study tackled serotonin quantification in urine using SERS by developing a framework with spectral processing, deep learning models, and an explainability method, achieving a mean absolute error of 0.15 μM and mean percentage error of 4.67% with a CNN. The result includes identifying six prediction contexts, three linked to serotonin, enabling potential biomarker discovery.
Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SERS through a novel SERS bio-quantification framework, including spectral processing, analyte quantification, and model explainability. To this end,serotonin quantification in urine media was assessed as a model task with 682 SERS spectra measured in a micromolar range using cucurbit[8]uril chemical spacers. A denoising autoencoder was utilized for spectral enhancement, and convolutional neural networks (CNN) and vision transformers were utilized for biomarker quantification. Lastly, a novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analysed using a convolutional neural network with a three-parameter logistic output layer (mean absolute error = 0.15 μM, mean percentage error = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six prediction contexts, of which three were associated with serotonin. The proposed framework could unlock a novel, untargeted hypothesis generating method of biomarker discovery considering the rapid and inexpensive nature of SERS measurements, and the potential to identify biomarkers from CRIME contexts.