Machine Learning and Omics Analysis in Aortic Aneurysm

• Published in: Angiology Vol. 75; no. 10; pp. 921 - 927
• Main Authors: Lareyre, Fabien, Chaudhuri, Arindam, Nasr, Bahaa, Raffort, Juliette
• Format: Book Review Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-11-2024
• Subjects: Aortic Aneurysm - genetics; Aortic Aneurysm - metabolism; Biomarkers; Deep Learning; Genomics; Humans; Life Sciences; Machine Learning; Metabolomics; Proteomics; deep learning; aortic aneurysm; thoracic aortic aneurysm; omics; biomarkers; machine learning; artificial intelligence; abdominal aortic aneurysm
• ISSN: 0003-3197; 1940-1574; 1940-1574
• DOI: 10.1177/00033197231206427

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.