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), have a significant negative impact on patient outcomes. Inadequate treatment and poor detection rates can result in cardiovascular complications and limb loss. There is great promise for improving the detection and treatment of PAD and other medical disorders through machine learning (ML) and artificial intelligence (AI) techniques. This paper highlights the use of field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) to implement the fundamental ideas of AI and ML, specifically in the treatment of PAD. It emphasizes how these technologies can enhance drug selection, improve patient care, and refine disease phenotyping. This paper also describes how the integration of AI and ML with FPGA and ASIC technology can provide accurate and effective solutions to complex medical challenges, representing a significant breakthrough in medical analytics.

Keywords

Application-Specific Integrated Circuits / Field-Programmable Gate Arrays / Machine Learning / Peripheral Arterial Disease

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