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The specific and sensitive quantification of sAFP across multiple aspects is a primary premise. This work constructs a functionalized gold\/silver nanocube\u2010encapsulated microgel (Au\/AgNC\u2010MG), which can specifically capture sAFP through dual aptamer\u2010based recognition and generate sensitive Raman fingerprints through the heterogeneous bimetallic SERS system. To further improve the specificity and quantifiability of sAFP detection in different scenarios, a series of machine learning (ML) algorithms, including a sAFP classification algorithm, a sAFP image\u2010processing algorithm, and a sAFP\u2010based clinical HCC risk assessment algorithm, were established for sAFP quantification, imaging of single\u2010cell secreted sAFP, and clinical HCC risk assessment from general check\u2010up to cirrhosis clinic patient populations. An online HCC Risk Assessment website is built for the convenience of practical clinical application. The constructed Au\/AgNC\u2010MG and established ML algorithms compose a general paradigm for HCC\u2010related laboratory research and clinical applications.<\/jats:p>","DOI":"10.1002\/smtd.70822","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:57:26Z","timestamp":1783321046000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2010Assisted SERS Quantification of Sialylated Alpha\u2010Fetoprotein: From Single\u2010Cell Analysis to Hepatocellular Carcinoma Risk Assessment"],"prefix":"10.1002","volume":"10","author":[{"given":"Yu","family":"Xiao","sequence":"first","affiliation":[{"name":"Department of Medical Laboratory Affiliated Hospital of Southwest Medical University  Luzhou China"},{"name":"State Key Laboratory of Analytical Chemistry For Life Science School of Chemistry Nanjing University  Nanjing 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