{"title":"An AI-assisted multiplex fluorescence sensing platform for grading diagnosis of Alzheimer's disease.","authors":"Yibiao Liu, Zhongzeng Zhou, Xingyun Liu, Jian Zeng, Qiong Liu, Tailin Xu","doi":"10.1039/d5tb01103e","DOIUrl":null,"url":null,"abstract":"<p><p>Blood-based biomarkers have become increasingly important for Alzheimer's disease (AD) diagnosis. However, due to individual variations, diagnostic accuracy using a single blood biomarker remains low, making it challenging to implement in large-scale AD screening efforts. Herein, we developed a multiplex fluorescent sensing platform for simultaneously measuring Aβ40, Aβ42, and P-tau181 in the blood, and constructed an artificial intelligence (AI) model. These three biomarkers were analyzed in 60 clinical samples: 15 healthy control, 15 subjective cognitive decline, 15 mild cognitive impairment, and 15 AD samples. The AI model based on these three biomarkers exhibited high predictive accuracy (91%), high positive predictive value (PPV) and low false rate (8.8%). The diagnostic accuracy and PPV of the AI model exceeded 90% for AD grading diagnosis in clinical samples. This study introduces a promising strategy for disease diagnosis and grading based on multi-biomarker analysis.</p>","PeriodicalId":94089,"journal":{"name":"Journal of materials chemistry. B","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of materials chemistry. B","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1039/d5tb01103e","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Blood-based biomarkers have become increasingly important for Alzheimer's disease (AD) diagnosis. However, due to individual variations, diagnostic accuracy using a single blood biomarker remains low, making it challenging to implement in large-scale AD screening efforts. Herein, we developed a multiplex fluorescent sensing platform for simultaneously measuring Aβ40, Aβ42, and P-tau181 in the blood, and constructed an artificial intelligence (AI) model. These three biomarkers were analyzed in 60 clinical samples: 15 healthy control, 15 subjective cognitive decline, 15 mild cognitive impairment, and 15 AD samples. The AI model based on these three biomarkers exhibited high predictive accuracy (91%), high positive predictive value (PPV) and low false rate (8.8%). The diagnostic accuracy and PPV of the AI model exceeded 90% for AD grading diagnosis in clinical samples. This study introduces a promising strategy for disease diagnosis and grading based on multi-biomarker analysis.
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