Clinical diagnosis of diabetes using machine learning and surface-enhanced Raman spectroscopy liquid biopsy: an exploratory study

Abstract

The impact of diabetes on global health is increasing, underscoring the need for early and accurate diagnosis to prevent severe complications. Nevertheless, conventional diagnostic approaches, such as glycated hemoglobin testing and oral glucose tolerance tests, often lack sensitivity or specificity, particularly for detecting the disease at an early stage. In this exploratory clinical study, we present a promising alternative, label-free surface-enhanced Raman spectroscopy (SERS), which enables rapid, non-invasive biochemical analysis of liquid samples. Using gold nanoparticles as substrates, we applied label-free SERS to clinical serum samples for diabetes diagnosis. Because label-free SERS analysis of biological samples yields complex spectra, we developed a machine learning workflow tailored to clinical samples, exploring four different machine learning models in combination with synthetic data augmentation. This approach achieved classification accuracies of 96% and 94% for the healthy and diabetes groups, respectively. Our results demonstrate the benefits of integrating label-free SERS and machine learning models for efficient, accurate diabetes diagnosis via liquid biopsy, offering a powerful tool to enhance detection and potentially improve patient outcomes worldwide.