预测受试者的进化IvE认知衰退到阿尔茨海默病 通过机器学习:PREVIEW研究协议
Salvatore Mazzeo1,2, Michael Lassi3, Sonia Padiglioni2,4
1Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy.
BMC neurology
|August 12, 2023
概括
这项研究使用机器学习来识别主观认知衰退 (SCD) 个体的早期阿尔茨海默病 (AD). 目标是开发具有成本效益的非侵入性工具,用于早期AD检测和治疗.
科学领域:
- 神经学 神经学
- 生物技术是生物技术.
- 数据科学数据科学数据科学
背景情况:
- 主观认知衰退 (SCD) 可能代表阿尔茨海默病 (AD) 的早期症状阶段.
- 随着疾病修饰疗法 (DMT) 的出现,准确识别早期的AD至关重要.
- 需要具有成本效益的,非侵入性的工具来检测那些正在发展为AD痴呆症的SCD患者.
研究的目的:
- 描述PREVIEW前性队列研究的协议.
- 识别可访问,非侵入性评估的特征,以检测高风险发展为AD痴呆症的SCD患者.
- 开发一种机器学习算法,用于预测SCD患者的AD风险.
主要方法:
- 招募被诊断患有SCD的患者.
- 进行全面的评估,包括临床,神经心理,遗传 (APOE,BDNF) 和电生理学评估.
- 进行腰部穿刺以测量AD生物标志物 (Aβ42,t-tau,p-tau) 并使用机器学习分析数据.
主要成果:
- 该研究将使用收集的数据来训练机器学习算法.
- 该算法将旨在确定SCD患者AD和痴呆症进展的风险.
- 预期的结果将为AD风险估计提供具有成本效益和可扩展的工具提供证据.
结论:
- 这项研究开创了机器学习用于预测SCD患者的AD.
- 这些发现可能会导致开发全球可扩展的,具有成本效益的工具,用于早期发现AD.
- 早期识别有风险的个体对于及时干预新出现的DMT至关重要.
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