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.

翻译：本研究收集了19例临床诊断为阿尔茨海默病(AD)的受试者及5例病理对照者的脑脊液(CSF)，并采用拉曼光谱(RS)进行分析。我们探究了原始及预处理后的拉曼光谱能否用于区分AD患者与对照组。首先，我们应用标准机器学习(ML)方法，但结果不理想。随后，我们将ML应用于从原始光谱中提取的一组拓扑描述符，获得了极佳的分类准确率(>87%)。尽管结果尚属初步，但表明RS与拓扑分析相结合可能为确认或否定AD的临床诊断提供有效途径。后续步骤将包括扩大CSF样本数据集以更好地验证所提出的方法，并可能探究拓扑数据分析能否辅助区分AD亚型。