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

TL;DR

This study explores the use of Raman spectroscopy combined with topological machine learning to distinguish Alzheimer's disease from controls using cerebrospinal fluid samples, showing promising preliminary results.

Contribution

The paper introduces a novel approach applying topological data analysis to Raman spectra for improved AD classification accuracy.

Findings

Achieved over 87% classification accuracy using topological descriptors

Standard ML methods yielded unsatisfactory results on raw spectra

Preliminary results suggest potential for non-invasive AD diagnosis

Abstract

The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.