Diagnosing Malaria Prone Areas in kenya Using Artificial intelligence
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Artificial Intelligence Offers a Better Way to Diagnose Malaria An algorithm for spotting malaria under the microscope could bring accurate, rapid diagnosis to understaffed areas. Current diagnosis of malaria relies on two approaches: microscopy and rapid diagnostic tests. Rapid diagnostic tests are portable cards that display bands in the presence of malaria, much like an at-home pregnancy test. They’re inexpensive, but even a small cost can be prohibitive. By contrast, once a clinic has a microscope and some glass slides, they can be reused indefinitely without further costs. ...learn more
Project status: Concept
Robotics, RealSense™, Internet of Things, Artificial Intelligence
Intel Technologies
Other
Overview / Usage
For all our efforts to control malaria, diagnosing it in many parts of the world still requires counting malaria parasites under the microscope on a glass slide smeared with blood. Now an artificial intelligence program can do it more reliably than most humans.
Methodology / Approach
A microscope with an attached laptop running a software algorithm, uses deep learning to analyze microscope images. Deep-learning software uses artificial neural networks mimicking the brain to allow computers to recognize abstract patterns. The Software will be trained on 120 slides from collections around the Kenya, both with and without malaria. The software will use visual features like shape, color, and texture to calculate the probability that a given object is a malaria parasite. It classified 170 samples during the field testing.
Technologies Used
Artificial intelligence
Visualization
Machine learning
Software and Hardware Development
Internet of things
Intel Technologies