Custom hardware design for peripheral artery disease detection: Field-programmable gate arrays and application-specific integrated circuits

Abstract

Atherosclerotic disorders such as peripheral artery disease (PAD) has a major negative influence on patient outcomes. Inadequate treatment and a poor detection rate can result in cardiovascular problems and limb loss. There is great promise for improving the detection and treatment of PAD and other medical disorders with machine learning (ML) and artificial intelligence (AI) techniques. Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) are used to implement the fundamental ideas of AI and ML, as this study highlights. We look at how these technologies are used for PAD, highlighting how they may be used to better pick drugs, improve patient care, and improve disease phenotype. Providing accurate and effective solutions for difficult medical problems, the fusion of AI and ML with FPGA and ASIC technology represents a significant breakthrough in medical analytics.