Identification of hemodynamic biomarkers for bicuspid aortic valve induced aortic dilation using machine learning

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dc.contributor.authorFranco, Pamela
dc.contributor.authorSotelo, Julio
dc.contributor.authorGuala
dc.contributor.authorDux-Santo, Lydia
dc.contributor.authorEvangelista, Arturo
dc.contributor.authorRodríguez-Palomares, José
dc.contributor.authorMery, Domingo
dc.contributor.authorSalas.Rodrigo
dc.contributor.authorUribe, Sergio
dc.date.accessioned2022-11-30T02:46:21Z
dc.date.available2022-11-30T02:46:21Z
dc.date.issued2022
dc.description.abstractRecent advances in medical imaging have confirmed the presence of altered hemodynamics in bicuspid aortic valve (BAV) patients. Therefore, there is a need for new hemodynamic biomarkers to refine disease monitoring and improve patient risk stratification. This research aims to analyze and extract multiple correlation patterns of hemodynamic parameters from 4D Flow MRI data and find which parameters allow an accurate classification between healthy volunteers (HV) and BAV patients with dilated and non-dilated ascending aorta using machine learning. Sixteen hemodynamic parameters were calculated in the ascending aorta (AAo) and aortic arch (AArch) at peak systole from 4D Flow MRI. We used sequential forward selection (SFS) and principal component analysis (PCA) as feature selection algorithms. Then, eleven machine-learning classifiers were implemented to separate HV and BAV patients (non- and dilated ascending aorta). Multiple correlation patterns from hemodynamic parameters were extracted using hierarchical clustering. The linear discriminant analysis and random forest are the best performing classifiers, using five hemodynamic parameters selected with SFS (velocity angle, forward velocity, vorticity, and backward velocity in AAo; and helicity density in AArch) a 96.31 ± 1.76% and 96.00 ± 0.83% accuracy, respectively. Hierarchical clustering revealed three groups of correlated features. According to this analysis, we observed that features selected by SFS have a better performance than those selected by PCA because the five selected parameters were distributed according to 3 different clusters. Based on the proposed method, we concluded that the feature selection method found five potentially hemodynamic biomarkers related to this disease.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameFranco_Ide2022.pdf
dc.identifier.doihttps://doi.org/10.1016/j.compbiomed.2021.105147
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7332
dc.languageen
dc.publisherElsevier
dc.sourceComputers in Biology and Medicine
dc.subjectBICUSPID AORTIC VALVEen_ES
dc.subjectHEMODYNAMIC BIOMARKERen_ES
dc.subjectMACHINE LEARNINGen_ES
dc.subjectPATTERN RECOGNITIONen_ES
dc.subjectFEATURE SELECTIONen_ES
dc.titleIdentification of hemodynamic biomarkers for bicuspid aortic valve induced aortic dilation using machine learning
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Biomedica
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