Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms

Abstract It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomat...

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Autores principales:	Moritz Lindquist Liljeqvist, Marko Bogdanovic, Antti Siika, T. Christian Gasser, Rebecka Hultgren, Joy Roy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f7c92745df1c4f6296f720b43f137127
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spelling	oai:doaj.org-article:f7c92745df1c4f6296f720b43f1371272021-12-02T14:58:47ZGeometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms10.1038/s41598-021-96512-32045-2322https://doaj.org/article/f7c92745df1c4f6296f720b43f1371272021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96512-3https://doaj.org/toc/2045-2322Abstract It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomatic aneurysm. 189 patients with AAAs of diameters 40–50 mm were included, 161 had undergone two CTAs. Geometric and biomechanical variables were used in prediction modelling. Classifications were evaluated with area under receiver operating characteristic curve (AUC) and regressions with correlation between observed and predicted growth rates. Compared with the baseline clinical diameter, geometric-biomechanical analysis improved prediction of reaching surgical threshold within four years (AUC 0.80 vs 0.85, p = 0.031) and prediction of diameter growth rate (r = 0.17 vs r = 0.38, p = 0.0031), mainly due to the addition of semiautomatic diameter measurements. There was a trend towards increased precision of volume growth rate prediction (r = 0.37 vs r = 0.45, p = 0.081). Lumen diameter and biomechanical indices were the only variables that could predict future rupture or symptomatic AAA (AUCs 0.65–0.67). Enhanced precision of diameter measurements improves the prediction of reaching the surgical threshold and diameter growth rate, while lumen diameter and biomechanical analysis predicts rupture or symptomatic AAA.Moritz Lindquist LiljeqvistMarko BogdanovicAntti SiikaT. Christian GasserRebecka HultgrenJoy RoyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Moritz Lindquist Liljeqvist Marko Bogdanovic Antti Siika T. Christian Gasser Rebecka Hultgren Joy Roy Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
description	Abstract It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomatic aneurysm. 189 patients with AAAs of diameters 40–50 mm were included, 161 had undergone two CTAs. Geometric and biomechanical variables were used in prediction modelling. Classifications were evaluated with area under receiver operating characteristic curve (AUC) and regressions with correlation between observed and predicted growth rates. Compared with the baseline clinical diameter, geometric-biomechanical analysis improved prediction of reaching surgical threshold within four years (AUC 0.80 vs 0.85, p = 0.031) and prediction of diameter growth rate (r = 0.17 vs r = 0.38, p = 0.0031), mainly due to the addition of semiautomatic diameter measurements. There was a trend towards increased precision of volume growth rate prediction (r = 0.37 vs r = 0.45, p = 0.081). Lumen diameter and biomechanical indices were the only variables that could predict future rupture or symptomatic AAA (AUCs 0.65–0.67). Enhanced precision of diameter measurements improves the prediction of reaching the surgical threshold and diameter growth rate, while lumen diameter and biomechanical analysis predicts rupture or symptomatic AAA.
format	article
author	Moritz Lindquist Liljeqvist Marko Bogdanovic Antti Siika T. Christian Gasser Rebecka Hultgren Joy Roy
author_facet	Moritz Lindquist Liljeqvist Marko Bogdanovic Antti Siika T. Christian Gasser Rebecka Hultgren Joy Roy
author_sort	Moritz Lindquist Liljeqvist
title	Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
title_short	Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
title_full	Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
title_fullStr	Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
title_full_unstemmed	Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
title_sort	geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f7c92745df1c4f6296f720b43f137127
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Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms

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