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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 395

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, 950 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 208
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Vascular Imaging: Advances, Applications, and Future Perspectives)
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