- Open Access
- Article
Advanced Cloud-Based Solutions for Peripheral Artery Disease: Diagnosis, Analysis, and Visualization
by Mohammed A. AboArab 1,2 
, Vassiliki T. Potsika 1 and Dimitrios I. Fotiadis 1, 3
1 Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
2 Electronics and Electrical Communication Engineering Dept., Faculty of Engineering, Tanta University, Tanta, Egypt
3 Biomedical Research Institute, Foundation for Research and Technology-Hellas, University Campus of Ioannina, Ioannina, GR45110, Greece
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 12, Page # 24-35, 2024; DOI: 10.55708/js0312003
Keywords: Digital health technologies, peripheral artery disease, cloud computing, medical imaging visualization, noninvasive diagnostics
Received: 24 September 2024, Revised: 22 November 2024, Accepted: 23 November 2024, Published Online: 18 December 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Aboarab, M. A., Potsika, V. T., & Fotiadis, D. I. (2024). Advanced cloud-based solutions for peripheral artery disease: Diagnosis, analysis, and visualization. Journal of Engineering Research and Sciences, 3(12), 24–35. https://doi.org/10.55708/js0312003
Chicago/Turabian Style
Aboarab, Mohammed A., Vassiliki T. Potsika, and Dimitrios I. Fotiadis. “Advanced Cloud-Based Solutions for Peripheral Artery Disease: Diagnosis, Analysis, and Visualization.” Journal of Engineering Research and Sciences 3, no. 12 (2024): 24–35. https://doi.org/10.55708/js0312003.
IEEE Style
M. A. Aboarab, V. T. Potsika, and D. I. Fotiadis, “Advanced cloud-based solutions for peripheral artery disease: Diagnosis, analysis, and visualization,” Journal of Engineering Research and Sciences, vol. 3, no. 12, pp. 24–35, 2024, doi: 10.55708/js0312003.
Peripheral artery disease (PAD) affects 237 million people globally, leading to significant morbidity and mortality. Traditional diagnostic methods are invasive, costly, and require specialized expertise, emphasizing the need for more accessible, and accurate alternatives. This paper introduces the DECODE cloud platform, an advanced tool that leverages cloud computing, machine learning, and high-performance data visualization to enhance PAD diagnosis and treatment. The platform integrates modules for peripheral artery segmentation, reconstruction, and comprehensive data warehousing, supporting 2D and three-dimensional (3D) rendering visualization. It enables the simulation and optimization of drug-coated balloons, enhancing clinical decision-making through robust data analytics. The evaluation metrics demonstrate the platform’s efficacy: the multiplanar visualization module achieved a performance score 94%, and the 3D rendering module scored 89%, with both modules attaining perfect scores in best practices and search engine optimization. These results highlight the DECODE platform’s capacity to provide scalable, noninvasive diagnostic solutions, setting a new standard in digital health technologies for PAD. This study underscores the transformative potential of integrating advanced visualization and computing techniques in medical diagnostics.
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