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Volume 14
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach

by
Panagiotis K. Siogkas
1,
Dimitrios Pleouras
1,
Vasileios Pezoulas
1,
Vassiliki Kigka
1,
Vassilis Tsakanikas
1,
Evangelos Fotiou
1,
Vassiliki Potsika
1,
George Charalampopoulos
2,
George Galyfos
2,
Fragkiska Sigala
2,
Igor Koncar
3 and
Dimitrios I. Fotiadis
1,4,*
1
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
2
First Propedeutic Department of Surgery, National and Kapodistrian University of Athens, 11527 Athens, Greece
3
Department of Vascular and Endovascular Surgery, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
4
Biomedical Research Institute—Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(19), 2204; https://doi.org/10.3390/diagnostics14192204
Submission received: 13 August 2024 / Revised: 26 September 2024 / Accepted: 29 September 2024 / Published: 2 October 2024
(This article belongs to the Special Issue Vascular Imaging: Advances, Applications, and Future Perspectives)
Versions Notes

Abstract

In order to predict cerebrovascular event occurrences, this work introduces a novel method that combines computational fluid dynamics (CFD), structural analysis, and machine learning (ML). The study presents a multidisciplinary approach to evaluate the risk of carotid atherosclerosis and cerebrovascular event prediction by utilizing both imaging and non-imaging data. The study uses blood-flow simulations and 3D reconstruction techniques to identify important properties of plaque that may indicate cerebrovascular events. The analysis shows high accuracy of the model in predicting these events and is validated on a dataset of 134 asymptomatic individuals with carotid artery disease. The goal of this work is to improve clinical decision-making by providing a tool that blends machine learning algorithms, structural analysis, and CFD. The dataset imbalance was treated with two approaches in order to select the optimal one for the training of the Gradient Boosting Tree (GBT) classifier. The best GBT model yielded a balanced accuracy of 88%, having a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91.
Keywords: computational fluid dynamics (CFD); machine learning (ML); cerebrovascular events computational fluid dynamics (CFD); machine learning (ML); cerebrovascular events

Share and Cite

MDPI and ACS Style

Siogkas, P.K.; Pleouras, D.; Pezoulas, V.; Kigka, V.; Tsakanikas, V.; Fotiou, E.; Potsika, V.; Charalampopoulos, G.; Galyfos, G.; Sigala, F.; et al. Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach. Diagnostics 2024, 14, 2204. https://doi.org/10.3390/diagnostics14192204

AMA Style

Siogkas PK, Pleouras D, Pezoulas V, Kigka V, Tsakanikas V, Fotiou E, Potsika V, Charalampopoulos G, Galyfos G, Sigala F, et al. Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach. Diagnostics. 2024; 14(19):2204. https://doi.org/10.3390/diagnostics14192204

Chicago/Turabian Style

Siogkas, Panagiotis K., Dimitrios Pleouras, Vasileios Pezoulas, Vassiliki Kigka, Vassilis Tsakanikas, Evangelos Fotiou, Vassiliki Potsika, George Charalampopoulos, George Galyfos, Fragkiska Sigala, and et al. 2024. "Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach" Diagnostics 14, no. 19: 2204. https://doi.org/10.3390/diagnostics14192204

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