Recent Advancements in Detection and Quantification of Malaria Using Artificial Intelligence

Abstract

Study’s Novelty/Excerpt • A review of recent advancements in artificial intelligence (AI)-based techniques, convolutional neural networks (CNNs) and deep learning, for malaria detection and quantification, is presented. • The strengths and limitations of AI approaches in analyzing digital images and blood smears as well as current challenges, including dataset scarcity and algorithm robustness were explored. • The potential scalability of AI-powered systems in resource-limited areas is discussed so as to provide insights to the future of AI-assisted malaria diagnostics and global disease control strategies. Full Abstract Plasmodium parasites are the principal causative agents of malaria, a highly infectious and sometimes fatal illness. It is a serious worldwide health risk, particularly in tropical and subtropical areas, where it has become a significant public health threat. Thus, its diagnosis must be timely, efficient, and accurate to allow suitable management and effective control of the disease. With recent technological advancements, it became possible to use current advances in image processing and machine learning to apply artificial intelligence (AI) for the detection /quantification of malaria parasites. The goal of this paper is to present a thorough analysis of the most advanced AI-assisted techniques available today, such as convolutional neural networks (CNNs), deep learning, and computer vision approaches, highlighting their strengths and limitations for identifying and quantifying malaria parasites in a variety of biological materials, including digital photos and blood smears. The review also discusses key challenges and future trends in AI-powered malaria detection, such as dataset scarcity, stability and robustness of algorithms, and scalability at a geographic level for resource-constraining areas. In conclusion, through critically examining the existing literature and research findings, this review showcases the potential of AI-driven technologies to revolutionize malaria diagnosis and surveillance with a view to guiding stakeholders in the choice of effective control strategies against this infectious disease.

Keywords

• artificial intelligence
• convolutional neural networks
• detection
• quantification
• malaria
• plasmodium falciparum