Artificial Intelligence (AI) in the Fight Against Malaria
Virtual Lab (VLab) Machine Learning is involved! Our approach is based on image segmentation. It is established that image segmentation is the process of separating a digital image into multiple sets of pixels (also called segments or image objects) and is a prerequisite step to further image analysis to locate specific objects of interest. Our machine learning algorithms is able to: - Labelling pixels and identifying regions of interest (ROIs), - Provide objects of interest find in the image, - Automatically analyzing full datasets and specifying objects detected. It is undeniable that using automated image analysis to identify structures of interest makes the process exponentially faster and more efficient.
The VCHF system is a web-based medical decision support system specifically for malaria diagnosis and therapy to assist Healthcare professionals at medical consultation in order to optimize the quality of care of the patients with malaria disease. The system is built around agents, Intelligent Software, who are endowed with the capacities allowing them to perform the tasks contributing to the Optimization of the Management of Malaria. Our system is using Machine Learning to analyze virtual slide for the specification of the type of Plasmodium. The main components of VCHF are: 1° eConsultation Component: The eConsultation process follows the primary logic of the hospital journey. Beginning by the admission, it continues with clinical investigations, laboratory examinations, complementary examinations, medical decisions and treatment to achieve at a complete medical record. 2° Virtual Lab Component: It provides an automated diagnosis of Plasmodium species. 3° Pharmacotherapy component: It provides useful information related malaria pharmacology. 4° Smart Health Information component: It provides clinical cases and helps to visualize scenarios, to extent intelligence, to make more informed decisions and to address complex issues. 5° Ontology component: It provides collections of related terms that help to consolidate malaria knowledge. 6° eLearning component: It provides information and training through scientific articles and case studies.