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The cerebrospinal fluid (CSF) of 19 subjects who received a clinicaldiagnosis of Alzheimer's disease (AD) as well as of 5 pathological controlshave been collected and analysed by Raman spectroscopy (RS). We investigatedwhether the raw and preprocessed Raman spectra could be used to distinguish ADfrom controls. First, we applied standard Machine Learning (ML) methodsobtaining unsatisfactory results. Then, we applied ML to a set of topologicaldescriptors extracted from raw spectra, achieving a very good classificationaccuracy (>87%). Although our results are preliminary, they indicate that RSand topological analysis together may provide an effective combination toconfirm or disprove a clinical diagnosis of AD. The next steps will includeenlarging the dataset of CSF samples to validate the proposed method betterand, possibly, to understand if topological data analysis could support thecharacterization of AD subtypes.

Alzheimer Disease Detection from Raman Spectroscopy of the Cerebrospinal Fluid via Topological Machine Learning