AI Detects Parasite from Photos of Blood Samples Taken with Smartphone

 Endemic in 21 countries in the Americas, Chagas disease affects some six million people, with an annual incidence of 30,000 new cases in the region, leading to 14,000 deaths per year on average. Some 70 million people are estimated to risk contracting the disease because they live in areas exposed to triatomines. One of the techniques used to diagnose Chagas is performed by microscopists trained to detect the parasite in blood samples. This requires a professional microscope, which can be coupled to a high-resolution camera, but the method tends to be too expensive and unaffordable for low-income patients. Now, a new study has shown that artificial intelligence (AI) can be used to detect Trypanosoma cruzi in images of blood samples taken with a smartphone camera and analyzed by optical microscope.

The machine learning approach developed by researchers at the University of São Paulo (São Paulo, Brazil) was based on a random forest algorithm trained to detect and count T. cruzi trypomastigotes in mobile phone images. Trypomastigotes are the extracellular form of the protozoan and the only stage that circulates in the bloodstream of patients with acute Chagas. Images of blood smear samples taken with a camera capable of 12 megapixel resolution were analyzed to arrive at a set of features common to 1,314 parasites, including morphometric parameters (shape and size), color and texture.