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Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning
Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new‐onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non‐invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle‐enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non‐invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.
Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning
Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new‐onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non‐invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle‐enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non‐invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.
Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning
Weng, Ruiyuan (Autor:in) / Xu, Yudian (Autor:in) / Gao, Xinjie (Autor:in) / Cao, Linlin (Autor:in) / Su, Jiabin (Autor:in) / Yang, Heng (Autor:in) / Li, He (Autor:in) / Ding, Chenhuan (Autor:in) / Pu, Jun (Autor:in) / Zhang, Meng (Autor:in)
Advanced Science ; 12
01.02.2025
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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