Advanced Cloud-Based Solutions for Peripheral Artery Disease: Diagnosis, Analysis, and Visualization

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.