Abstract Alzheimer's disease (AD) is a progressive incurable neurodegenerative disease and a major health problem in aging population. We show that the combined use of Laser-Induced Breakdown Spectroscopy (LIBS) and machine learning applied for the analysis of micro-drops of plasma samples of AD and healthy controls (HC) yields robust classification. Following the acquisition of LIBS spectra of 67 plasma samples from a cohort of 31 AD patients and 36 healthy controls (HC), we successfully diagnose late-onset AD (> 65 years old), with a total classification accuracy of 80%, a specificity of 75% and a sensitivity of 85%.

中文翻译：

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使用激光诱导击穿光谱和机器学习诊断阿尔茨海默病

摘要 阿尔茨海默病（AD）是一种进行性、不可治愈的神经退行性疾病，也是老龄化人口中的一个主要健康问题。我们表明，结合使用激光诱导击穿光谱 (LIBS) 和机器学习来分析 AD 和健康对照 (HC) 血浆样本的微滴，可以产生可靠的分类。从 31 名 AD 患者和 36 名健康对照者 (HC) 的队列中获取 67 个血浆样本的 LIBS 光谱后，我们成功诊断了迟发性 AD（> 65 岁），总分类准确度为 80%，特异性75%，灵敏度85%。