AI-assisted FTIR spectroscopic profiling of exosomes: Emerging frontiers in early detection of Alzheimer's disease.

Abstract

Alzheimer's disease (AD) remains one of the most challenging neurodegenerative disorders, primarily due to the lack of reliable tools for its early and non-invasive diagnosis. Exosomes, nanosized extracellular vesicles secreted by neural and peripheral cells, have emerged as promising biomarkers reflecting the molecular alterations associated with AD pathogenesis. Fourier Transform Infrared (FTIR) spectroscopy, with its capacity to capture unique biochemical fingerprints of biomolecules, provides a rapid, label-free, and cost-effective approach for exosome characterization. The recent integration of Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, has significantly advanced the interpretation of complex FTIR spectra, enabling the identification of subtle spectral variations linked to disease progression. This review highlights the synergistic potential of AI-assisted FTIR spectroscopy for exosomal profiling in AD, discussing advances in spectral data analytics, biomarker discovery, and diagnostic modeling. Furthermore, it explores current challenges, technological gaps, and future perspectives toward establishing intelligent, exosome-based diagnostic frameworks for the early detection and personalized management of Alzheimer's disease.