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Diagnostics
Special Issues
Vascular Imaging: Advances, Applications, and Future Perspectives
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Vascular Imaging: Advances, Applications, and Future Perspectives

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 897

Special Issue Editor

Prof. Dr. Dimitrios I. Fotiadis

E-Mail Website
Guest Editor
1. Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, 45110 Ioannina, Greece
2. Department of Economics, University of Ioannina, 45110 Ioannina, Greece
3. Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110 Ioannina, Greece
Interests: automated diagnosis; processing of big medical data; sensor informatics; image informatics; bioinformatics

Special Issue Information

Dear Colleagues,

This Special Issue delves into the diverse applications of vascular imaging across medical fields, highlighting its crucial role in diagnosing and managing vascular diseases. Furthermore, it presents insights into the future perspectives of this rapidly evolving field, discussing potential breakthroughs and their impact on patient care. Through a collection of expert-authored articles, this Special Issue aims to enhance the understanding of vascular imaging among medical professionals, researchers, and students, paving the way for further innovations in vascular healthcare.

Prof. Dr. Dimitrios I. Fotiadis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vascular imaging
  • medical
  • diagnosis
  • prognosis
  • healthcare

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Published Papers (1 paper)

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Research

17 pages, 5040 KiB  
Article
Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach
by Panagiotis K. Siogkas, Dimitrios Pleouras, Vasileios Pezoulas, Vassiliki Kigka, Vassilis Tsakanikas, Evangelos Fotiou, Vassiliki Potsika, George Charalampopoulos, George Galyfos, Fragkiska Sigala, Igor Koncar and Dimitrios I. Fotiadis
Diagnostics 2024, 14(19), 2204; https://doi.org/10.3390/diagnostics14192204 - 2 Oct 2024
Viewed by 683
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Vascular Imaging: Advances, Applications, and Future Perspectives)
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