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https://olympias.lib.uoi.gr/jspui/handle/123456789/39178Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Abo Arab, Mohammed | en |
| dc.date.accessioned | 2025-07-08T07:19:22Z | - |
| dc.date.available | 2025-07-08T07:19:22Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/39178 | - |
| dc.relation | info:eu-repo/grantAgreement/EC/H2020/956470/EU | el |
| dc.rights | CC0 1.0 Universal | * |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
| dc.subject | Peripheral artery disease (PAD); Real-time visualization; Volume rendering; Multiplanar reconstruction; Risk stratification; Progressive web application (PWA); Cloud-based medical imaging; Plaque density analysis; Clinical decision making | en |
| dc.title | A 3d-enabled Visual Representation Tool as a Progressive Web Application (PWA) based on the WebAssembly specification | en |
| dc.type | doctoralThesis | * |
| heal.type | doctoralThesis | el |
| heal.type.en | Doctoral thesis | en |
| heal.type.el | Διδακτορική διατριβή | el |
| heal.secondaryTitle | Cloud-Based Solutions for Peripheral Artery Disease: Data Analysis and Visualization | el |
| heal.classification | Biomedical Engineering | |
| heal.dateAvailable | 2025-07-08T07:20:22Z | - |
| heal.language | en | el |
| heal.access | free | el |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Πολυτεχνική Σχολή | el |
| heal.recordProvider | Materials Science and Engineering | en |
| heal.publicationDate | 2025-06-13 | - |
| heal.abstract | This thesis focuses on the development of advanced cloud-based medical imaging frameworks, high-fidelity visualization techniques, and computational modeling solutions for the noninvasive management of peripheral artery disease (PAD). The primary objective is to bridge critical gaps in diagnostic precision, clinical workflow efficiency, and personalized treatment strategies by leveraging web technologies, deep learning models, and computational simulations. This thesis introduces the DECODE Cloud Platform, an open-source cloud-based ecosystem that integrates AI-powered vascular segmentation, real-time 3D visualization, and predictive modeling for PAD risk assessment and treatment planning. The first chapter provides a detailed introduction to PAD, its pathophysiology, and the limitations of current diagnostic and treatment approaches, highlighting the need for innovative computational solutions. It outlines the role of web-based imaging, cloud computing, and AI in advancing PAD diagnostics and the foundation for research objectives. The second chapter presents a comprehensive review of state-of-the-art technologies in digital health, web-based medical imaging, and cloud-based platforms for PAD management. It explores advancements in web visualization, AI-powered vascular segmentation, and computational hemodynamics, establishing the theoretical background for the research. In addition, it discusses emerging trends and limitations in noninvasive vascular imaging and introduces the novel contributions of this work. The third chapter examines the computational modeling of drug-eluting balloons (DEBs) for PAD treatment. A systematic analysis of fluid‒structure interaction (FSI), molecular dynamics (MD), finite element modeling (FEM), and machine learning (ML) techniques is conducted to optimize drug diffusion, vascular response, and patient-specific intervention planning. This chapter explores how computational simulations enhance DEB design and performance, addressing therapeutic efficacy and in-silico validation. The fourth chapter focuses on advancing progressive web applications (PWAs) for medical imaging visualization, particularly DICOM and multiplanar reconstruction (MPR) visualization. It presents the technical architecture, algorithmic enhancements, and performance evaluations of the system. Key innovations include the implementation of bicubic and weighted bilinear interpolation techniques, ensuring high-precision 3D reconstructions, cross-platform compatibility, and offline-accessible imaging workflows. The fifth chapter introduces DECODE-3DViz, a WebGL-based high-fidelity visualization platform optimized for large-scale peripheral artery CT imaging. The research addresses WebGL texture constraints and real-time performance bottlenecks by integrating a level-of-detail (LOD) algorithm, dynamic downsampling, and data chunk streaming. In addition, this chapter presents the automated PAD risk classification framework, which employs optimized volume rendering, dynamic illumination, and quantitative vascular analysis to improve diagnostic accuracy and clinical decision support. A detailed performance validation study demonstrated the efficacy of DECODE-3DViz in enabling interactive 3D visualization for vascular diagnostics. The sixth chapter details the DECODE Cloud Platform, an open-source, cloud-native infrastructure designed for AI-driven PAD diagnostics and in-silico clinical trials. It integrates deep learning-based vascular segmentation, computational hemodynamic modeling, and real-time 3D visualization, providing a scalable, regulatory-compliant framework for multi-institutional collaboration. The usability evaluation via the System Usability Scale (SUS) and Technology Acceptance Model (TAM) confirms high adoption potential, underscoring its clinical viability and integration into real-world medical workflows. The seventh chapter presents the conclusions and future directions of this research. This thesis highlights the impact of AI-driven vascular imaging, web-based visualization, and computational modeling in redefining PAD diagnostics and noninvasive therapeutic planning. Future research will focus on WebGPU-enhanced visualization, AI-driven multimodal fusion (CT, MRI, and ultrasound), federated learning for privacy-preserving AI training, and real-time in-silico simulations for optimizing drug-coated balloon (DCB) therapy. The integration of blockchain-based regulatory compliance mechanisms and automated AI-generated radiology reports will further expand the clinical adoption of DECODE, ensuring its role as a pioneering platform in AI-assisted precision vascular medicine. The main contributions of this thesis can be summarized as follows: (i) The development of a web-based DICOM and MPR visualization system within a PWA framework ensures cross-platform compatibility, offline accessibility, and optimized real-time rendering for high-resolution vascular imaging. (ii) The introduction of DECODE-3DViz, a high-fidelity WebGL-based visualization pipeline that incorporates LOD algorithms and chunk streaming, significantly enhances real-time interactive visualization of large-scale CT images while optimizing GPU memory and performance efficiency. (iii) The design and validation of an automated PAD risk classification framework integrating dynamic illumination models, optimized volume rendering, and quantitative vascular analysis improve diagnostic accuracy, reduce interobserver variability, and enable real-time clinical decision support. (iv) The application of computational modeling techniques for DEBs, utilizing FSI, MD, and finite element simulations, to enhance drug delivery, optimize device performance, and advance patient-specific treatment strategies. (v) DECODE, an open-source cloud-based platform that integrates AI-driven vascular segmentation, computational hemodynamic modeling, and real-time 3D visualization, ensures scalability, interoperability, and seamless clinical integration into digital healthcare ecosystems, was developed. This thesis establishes a new benchmark in cloud-based vascular imaging, risk classification, and computational modeling, providing a scalable and clinically viable solution for noninvasive PAD management and precision vascular medicine. | en |
| heal.sponsor | European Commission – Horizon 2020, Marie Skłodowska-Curie Actions (Grant Agreement No. 956470) | en |
| heal.advisorName | Fotiadis, 1. Dimitrios | en |
| heal.committeeMemberName | Fotiadis, 1. Dimitrios | en |
| heal.committeeMemberName | Gergidis, 2. Leonidas | en |
| heal.committeeMemberName | Filipovic, 3. Nenad | en |
| heal.committeeMemberName | Goletsis, 4. Yorgos | en |
| heal.committeeMemberName | Papaloukas, 5. Costas | en |
| heal.committeeMemberName | Exarchos, 6. Themis | en |
| heal.committeeMemberName | Skalski, 7. Andrzej | en |
| heal.academicPublisher | Πανεπιστήμιο Ιωαννίνων. Πολυτεχνική Σχολή. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.academicPublisher | Biomedical Engineering | en |
| heal.academicPublisherID | uoi | el |
| heal.numberOfPages | 247 | el |
| heal.fullTextAvailability | true | - |
| Appears in Collections: | Διδακτορικές Διατριβές - ΜΕΥ | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| final-signed-Mohammed-PHD-uoi-03-07-2025.pdf | A 3D-enabled visual representation tool as a progressive web application (PWA) based on the WebAssembly specification | 5.95 MB | Adobe PDF | View/Open |
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