In a recent study published in Scientific Reports , a team of researchers proposed using an artificial intelligence (AI) tool that uses deep learning to examine red blood cell images in blood smears for the timely detection of malaria. Study: Efficient deep learning-based approach for malaria detection using red blood cell smears . Image Credit: cones/Shutterstock.
com The World Health Organization report from 2015 shows that in subtropical and tropical regions of the world, the parasite of the genus Plasmodium that causes malaria was responsible for over 400,000 deaths. Malaria is usually detected through microscopic analysis of blood smear slides, which reveal infected erythrocytes or red blood cells. Given that regions in Africa, South East Asia, and the Mediterranean experience over 70% of malaria cases, the process of detecting malaria through blood smears becomes very laborious and significantly increases the pathologist’s workload.
AI-based tools involving machine learning and deep-learning approaches have been widely explored in recent studies for automated screening and applications in clinical diagnoses. However, traditional AI approaches such as neural networks have faced challenges in detecting and identifying malarial parasites in blood smears due to the small size and substantial disparity in blood cells. Furthermore, these methods still require qualified pathologists for feature vector extraction, making it difficult to automate the screening and detection pro.
