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使用血清代谢指纹和机器学习进行莫亚莫亚病的非侵入性诊断

Ruiyuan Weng^1,2, Yudian Xu^3,4, Xinjie Gao^1,2

¹Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, 200040, P. R. China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|December 31, 2024

概括

这项研究引入了一种非侵入性方法,用于使用血清代谢指纹和深度学习来诊断莫亚莫亚病 (MMD). 该方法准确地识别了MMD患者,有可能改善这种渐进性脑血管疾病的早期检测和治疗.

科学领域:

神经科学是一个神经科学.
生物化学生物化学
医学诊断医学诊断医学诊断

背景情况:

莫亚莫亚病 (MMD) 是一种进展性脑血管疾病,与中风,缺血和出血的风险增加有关.
目前的MMD诊断方法是侵入性的和昂贵的,有限的非侵入性生物标志物研究.
早期诊断和干预对于降低MMD患者中风风险至关重要.

研究的目的:

开发一种使用血清代谢指纹 (SMFs) 的莫亚莫亚病 (MMD) 非侵入性诊断方法.
建立基于深度学习算法的MMD诊断模型.
探索SMF和MMD中的血管认知障碍之间的关联.

主要方法:

使用纳米粒子增强激光脱吸/电离质谱法 (LDI MS) 来分析血清代谢指纹 (SMF).
开发了一种使用深度学习算法的诊断模型,以区分MMD患者与健康对照 (HC).
应用分层集群分析来研究SMF与认知功能之间的关系.

主要成果:

深度学习模型在将MMD与HC组区分方面取得了高准确性 (AUC = 0.958).
层次聚类揭示了MMD患者中SMF和血管认知障碍之间的显著关联.
LDI MS和深度学习方法显示出作为MMD新型诊断工具的潜力.

结论:

纳米粒子增强的LDI MS与深度学习相结合,为莫亚莫亚病提供了一个有希望的,非侵入性的诊断策略.
这种方法可以促进早期检测和干预,从而有可能改善患者的治疗结果.
这些发现表明,在诊断其他神经系统疾病方面,其应用范围更广.

关键词:

生物标志物生物标志物这些都是指纹,指纹.质谱测量质谱测量质谱测量质谱测量质量测量质谱测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量莫亚莫亚疾病诊断诊断

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