Machine Learning and Omics Analysis in Aortic Aneurysm

被引:5
作者
Lareyre, Fabien [1 ,2 ]
Chaudhuri, Arindam [3 ]
Nasr, Bahaa [4 ,5 ]
Raffort, Juliette [2 ,6 ,7 ]
机构
[1] Hosp Antibes Juan Les Pins, Dept Vasc Surg, 107 Ave Nice, F-06600 Antibes, France
[2] Univ Cote Azur, Inserm U1065, C3M, Nice, France
[3] Bedfordshire Hosp NHS Fdn Trust, Bedfordshire Milton Keynes Vasc Ctr, Bedford, England
[4] Brest Univ Hosp, Dept Vasc & Endovasc Surg, Brest, France
[5] LaTIM, INSERM UMR 1101, Brest, France
[6] Univ Hosp Nice, Clin Chem Lab, Nice, France
[7] Univ Cote Azur, 3IA Inst, Nice, France
关键词
machine learning; deep learning; artificial intelligence; aortic aneurysm; abdominal aortic aneurysm; thoracic aortic aneurysm; biomarkers; omics; ARTIFICIAL-INTELLIGENCE; ASSOCIATIONS; BIOMECHANICS; MECHANISMS; MORTALITY; DISEASES;
D O I
10.1177/00033197231206427
中图分类号
R6 [外科学];
学科分类号
1002 ; 100210 ;
摘要
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.
引用
收藏
页码:921 / 927
页数:7
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