{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T19:15:16Z","timestamp":1770923716511,"version":"3.50.1"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Most patients with ascending thoracic aortic aneurysms (ATAA) remain asymptomatic until they develop fatal complications, including aortic dissection and rupture. We aimed to develop and validate machine-learning models for predicting ATAA risk. We developed a predictive model for the risk of ATAA based on data from 18,382 participants from the Kangbuk Samsung Health Study between January 1, 2010, and December 31, 2018. In the screening context, an ATAA was defined as an ascending thoracic aorta with a diameter\u2009\u2265\u20093.7\u2009cm. For the model inputs, we used 16 variables from medical records, including basic patient information, physical indices, baseline medical conditions, and laboratory data at an early stage. A feature importance analysis was performed to analyze the factors related to the risk of ATAA in healthy adults. A machine learning model for predicting the risk of ATAA was developed using a 5-layer deep neural network (DNN) with the 15 key features. The performance of this model was evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Age was the most important factor in predicting the risk of ATAA, followed by hypertension, waist circumference, creatinine level, smoking, and body mass index. The AUROC and accuracy of our 5-layer DNN with the 15 key features are 80.4% and 83.5%, respectively. The sensitivity and specificity of the DNN were 69.4% and 81.1%, respectively. We developed and validated a machine learning model that can be used to assess the risk of ATAA. This model has potential applications in disease screening for ATAA at an early stage.<\/jats:p>","DOI":"10.1371\/journal.pone.0342482","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:23:19Z","timestamp":1770920599000},"page":"e0342482","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning"],"prefix":"10.1371","volume":"21","author":[{"given":"Seung Jae","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Hee","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eun-Ah","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suyong","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eun Jung","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1930-1378","authenticated-orcid":true,"given":"Jae-Geum","family":"Shim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"issue":"1","key":"pone.0342482.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11748-017-0874-x","article-title":"Thoracic aortic aneurysm: unlocking the \u201csilent killer\u201d secrets","volume":"67","author":"AA Saeyeldin","year":"2019","journal-title":"Gen Thorac Cardiovasc Surg"},{"issue":"5","key":"pone.0342482.ref002","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.ejvs.2016.01.017","article-title":"Systematic Review of the Growth Rates and Influencing Factors in Thoracic Aortic Aneurysms","volume":"51","author":"D Oladokun","year":"2016","journal-title":"Eur J Vasc Endovasc Surg"},{"key":"pone.0342482.ref003","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.amjcard.2022.06.032","article-title":"Predictors and Rate of Progression of Aortic Root and Ascending Aorta Dilatation","volume":"181","author":"A Eswarsingh","year":"2022","journal-title":"Am J Cardiol"},{"issue":"7","key":"pone.0342482.ref004","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.acra.2008.02.001","article-title":"Normal thoracic aorta diameter on cardiac computed tomography in healthy asymptomatic adults: impact of age and gender","volume":"15","author":"SS Mao","year":"2008","journal-title":"Acad Radiol"},{"issue":"4","key":"pone.0342482.ref005","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.19.21456","article-title":"Comparison of Motion Artifacts on CT Images Obtained in the Ultrafast Scan Mode and Conventional Scan Mode for Unconscious Patients in the Emergency Department","volume":"213","author":"T Takayanagi","year":"2019","journal-title":"AJR Am J Roentgenol"},{"issue":"6","key":"pone.0342482.ref006","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1177\/1358863X18807760","article-title":"Thoracic aortic aneurysm","volume":"23","author":"MJ Salameh","year":"2018","journal-title":"Vasc Med"},{"issue":"13","key":"pone.0342482.ref007","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the Future - 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