AccScience Publishing / IJOSI / Volume 9 / Issue 1 / DOI: 10.6977/IJoSI.202502_9(1).0007
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Custom hardware design for peripheral artery disease detection: Field-programmable gate arrays and application-specific integrated circuits

Nazarkar Pravalika1 ,  A. Jabeena1* ,  Vetriveeran Rajamani1
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1 School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
IJOSI 2025 , 9(1), 78–97; https://doi.org/10.6977/IJoSI.202502_9(1).0007
Submitted: 2 September 2024 | Revised: 21 October 2024 | Accepted: 30 October 2024 | Published: 19 February 2025
© 2025 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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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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