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Current diagnostic approaches, which rely on ultrasound scanning and blood measurements, are limited by low detection sensitivity. Herein, we propose a one\u2010step system that uses EP\u2010associated serum metabolic fingerprints (ESF) for efficient EP diagnosis and risk prediction. The system employs nanoparticle\u2010assisted laser desorption\/ionization mass spectrometry to rapidly record ESF. A machine learning\u2013based diagnostic model was then used to analyze ESF from 722 participants, achieving an area under the curve (AUC) of 0.913. Simultaneously, potential metabolic biomarkers from ESF were annotated, enabling accurate diagnosis of EP across various clinical profiles (AUC 0.922). Moreover, a rupture risk prediction model was constructed, yielding an AUC of 0.885, significantly surpassing conventional clinical indicators (AUC 0.702,\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.05). Our work offers a rapid, effective tool for early EP diagnosis and risk stratification, marking a pivotal advancement toward precision diagnostics.\n                  <\/jats:p>","DOI":"10.1002\/smtd.202501741","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:29:19Z","timestamp":1772962159000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Serum Metabolic Fingerprints for Ectopic Pregnancy Diagnosis and Rupture Risk Prediction"],"prefix":"10.1002","volume":"10","author":[{"given":"Juxiang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Gynecologic Oncology The International Peace Maternity and Child Health Hospital School of Medicine Shanghai Jiao Tong University  Shanghai P. R. 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