Deep learning algorithms and Raman spectroscopy in the clinical laboratory setting.

Abstract

Raman spectroscopy is an important diagnostic method that extracts molecular-level information from biological specimens, with distinct potential for disease diagnoses. However, its clinical application has been limited by the challenges associated with spectral interpretation. Deep learning (DL) represents an important new approach in which selected Raman spectroscopy experiments can be automated, offering the potential for higher classification accuracy. This paper highlights recent efforts toward the integration of Raman spectroscopy and DL for medical applications and elaborates on key DL models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), and Generative Adversarial Networks (GANs), which can collect relevant features, denoise spectra, and provide enhanced diagnostic value from biological specimens. The use of DL in Raman spectroscopy has produced impressive results in cancer diagnosis, bacterial identification, and viral diagnostics. Therefore, this paper provides an organized introduction to explore existing DL architectures used in Raman spectroscopy, their advantages and limitations, and opportunities for clinical applications. Collectively, DL with Raman spectroscopy provides a unique approach for noninvasive and reliable diagnostics.