The Role of Raman Spectroscopy and Artificial Intelligence in Pathophysiological Mechanisms and Diagnostic Advancements in Parkinson’s Disease

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder fundamentally characterized by the loss of dopaminergic neurons and the pathological aggregation of α-synuclein. A significant challenge persists in its early diagnosis, primarily due to the current lack of reliable, non-invasive biomarkers. Raman spectroscopy, a vibrational technique, offers a method for the label-free biochemical characterization of biological samples. When this technique is synergistically integrated with artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL) algorithms, the resultant Raman spectral data can be analyzed to achieve robust sensitivity and specificity. The present work introduces a comprehensive framework that integrates Raman spectroscopy with AI for both the diagnosis and therapeutic monitoring of PD. The established methodology encompasses spectral acquisition, meticulous preprocessing, efficient feature extraction, and subsequent classification utilizing advanced computational algorithms. Initial findings indicate diagnostic accuracies frequently surpassing 90%, accompanied by sensitivities and specificities consistently above 85%. Moreover, this system facilitates the monitoring of biochemical alterations occurring during therapeutic interventions. This holistic and integrated approach thus offers a promising direction for achieving earlier diagnosis and advancing precision medicine within the context of Parkinson's disease.