Artificial Intelligence and Raman Spectroscopic Application to Materials and Biomedical Research

 Abstract:

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized numerous fields, including biomedical research. Raman spectroscopy, a noninvasive and label free technique provides molecular composition and structural information of biomolecules.  The complicated spectral data produced by Raman spectroscopy, which can be difficult to interpret manually, can be rapidly and accurately analyzed with AI. Meaningful information can be extracted from large datasets, facilitating the identification and characterization of biological samples. ML models can also be trained to recognize spectral patterns associated with specific disease states or metabolic changes.

 In our research, we have used this synergy between Raman spectroscopy and AI to address some of the pressing challenges in the field of biomedicine and materials. Rapid and label free identification of pathogens at single cell level is the need of the hour in medical diagnostics. Conventional machine learning techniques fall short when it comes to identifying multiple classes of bacteria with similar molecular fingerprints. We have demonstrated that Raman spectroscopy can classify bacterial strains that are quite similar with high accuracy using AI methods. Moreover, we are actively exploring innovative deep learning frameworks for the identification of diseases such as COVID-19 and sepsis with high sensitivity. The rapid detection of COVID-19 through this approach has the potential to serve as a highly effective complementary technique to RT-PCR and enable mass screening at public venues to curb the spread of infection. In the case of sepsis, a life-threatening condition lacking a definitive diagnostic test, the integration of Raman spectroscopy with AI can help in identification of crucial biomarkers for both prognosis and diagnosis.