Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach
Panagiotis K. Siogkas,
Dimitrios Pleouras,
Vasileios Pezoulas,
Vassiliki Kigka,
Vassilis Tsakanikas,
Evangelos Fotiou,
Vassiliki Potsika,
George Charalampopoulos,
George Galyfos,
Fragkiska Sigala,
Igor Koncar,
Dimitrios I. Fotiadis
Affiliations
Panagiotis K. Siogkas
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Dimitrios Pleouras
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Vasileios Pezoulas
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Vassiliki Kigka
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Vassilis Tsakanikas
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Evangelos Fotiou
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Vassiliki Potsika
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
George Charalampopoulos
First Propedeutic Department of Surgery, National and Kapodistrian University of Athens, 11527 Athens, Greece
George Galyfos
First Propedeutic Department of Surgery, National and Kapodistrian University of Athens, 11527 Athens, Greece
Fragkiska Sigala
First Propedeutic Department of Surgery, National and Kapodistrian University of Athens, 11527 Athens, Greece
Igor Koncar
Department of Vascular and Endovascular Surgery, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
Dimitrios I. Fotiadis
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model’s performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model’s high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes.
