Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning

Seung Jae Lee; Jin Hee Ahn; Eun-Ah Cho; Suyong Jeon; Eun Jung Oh; Jae-Geum Shim

doi:10.1371/journal.pone.0342482

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Open Access

Peer-reviewed

Research Article

Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning

Seung Jae Lee ,

Contributed equally to this work with: Seung Jae Lee, Jin Hee Ahn

Roles Formal analysis, Writing – original draft

Affiliation Division of Cardiology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
⨯
Jin Hee Ahn ,

Contributed equally to this work with: Seung Jae Lee, Jin Hee Ahn

Roles Writing – original draft

Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
⨯
Eun-Ah Cho,

Roles Data curation, Investigation

Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
⨯
Suyong Jeon,

Roles Investigation, Methodology

Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
⨯
Eun Jung Oh,

Roles Investigation, Methodology

Affiliation Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
⨯
Jae-Geum Shim

Roles Conceptualization, Writing – review & editing

* E-mail: jgshim77@naver.com

Affiliations Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea, Healthcare Data Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

https://orcid.org/0000-0002-1930-1378

⨯

Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning

Seung Jae Lee,
Jin Hee Ahn,
Eun-Ah Cho,
Suyong Jeon,
Eun Jung Oh,
Jae-Geum Shim

Published: February 12, 2026
https://doi.org/10.1371/journal.pone.0342482

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