Raman Spectroscopy-Based, Non-Destructive Biomedical Diagnosis

Raman spectroscopy is a non-destructive, label-free analytical technique that can probe biochemical alterations in tissues and cells. Raman spectroscopy, being sensitive to biochemical perturbations, can potentially provide early and real-time identification of changes preceding morphological changes, allowing early diagnosis as well as disease monitoring. Recent research has demonstrated its broad utility across diverse clinical domains, including cancers, neurological conditions, and infections. Raman spectroscopy combined with machine learning algorithms allows rapid assessment and automated pipelines and can act as a clinical adjunct, enhanced by integrating tools like principal component analysis (PCA), linear discriminant analysis (LDA), random forests, and deep-learning architectures. These models allow interpretation of complex spectra, and decipher meaningful biomarkers in heterogeneous clinical samples. This review highlights the earliest and most recent progress in Raman-based non-destructive diagnosis, underscoring advances in cancer diagnosis and challenges faced in clinical settings.