Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information

Garcia Garcia, Fernando; Metaxa, Eleni; Christodoulidis, Stergios; Anthimopoulos, Marios; Kontopodis, Nikolaos; Correa-Londoño, Martina; Wyss, Thomas; Papacharilaou, Yiannis; Ioannou, Christos; Von Tengg-Kobligk, Hendrik; Mougiakakou, Stavroula Georgia (2017). Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information. In: 30th IEEE International Symposium on Computer-Based Medical Systems - IEEE CBMS 2017 (pp. 463-468). Institute of Electrical and Electronics Engineers 10.1109/CBMS.2017.158

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An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as ‘slow’, ‘medium’ or ‘quick’ growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (‘LPBoost’) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB
04 Faculty of Medicine > Other Institutions > Teaching Staff, Faculty of Medicine
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
04 Faculty of Medicine > Department of Cardiovascular Disorders (DHGE) > Clinic of Cardiovascular Surgery
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Garcia Garcia, Fernando; Christodoulidis, Stergios; Anthimopoulos, Marios; Correa-Londoño, Martina; Wyss, Thomas; Von Tengg-Kobligk, Hendrik and Mougiakakou, Stavroula Georgia

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISBN:

978-1-5386-1710-6

Publisher:

Institute of Electrical and Electronics Engineers

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

17 Aug 2017 15:46

Last Modified:

09 May 2018 14:08

Publisher DOI:

10.1109/CBMS.2017.158

BORIS DOI:

10.7892/boris.100045

URI:

https://boris.unibe.ch/id/eprint/100045

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