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Automated quantification of avian malaria parasitaemia in blood smears by artificial intelligence

Avian haemosporidians of the genus Plasmodium (referred to as avian malaria parasites) are mosquito-transmitted blood parasites which infect diverse birds species all over the world. Depending on the Plasmodium species and the affected bird hosts, clinical manifestations of avian malaria can range from asymptomatic (subclinical) infections to severe disease. Once the parasites have sucessfully multiplied in the organs of the infected bird, they get into the peripheral blood, called parasitaemia. In the blood, the parasites infect erythrocytes to continue their development and replication, leading to dynamic changes of parasitaemia intensities over the course of infection.Diagnosis of avian malaria is based on light microscopic detection of Plasmodium parasites in blood smears prepared from the bird host's blood. A key parameter to estimate severity of infection is parasitaemia intensity, defined as percentage of infected blood cells. Traditionally, calculation of parasitaemia intensity is done by manual counting of infected erythrocytes per several thousands of erythrocytes at high microscopic magnification, a procedure that is tedious and time-consuming. The aim of the present project is to use artificial intelligence to develop a computer-based image analysis algorithm, which allows automated quantification of Plasmodium-infected blood cells in avian blood smears. By applying deep learning, the state-of-the-art machine learning method, the algorithm will be trained to identify different developmental stages of the parasites in infected erythrocytes, and uninfected blood cells. Finally, the image analysis algorithm's performance (sensitivity, reproducability) will be compared to the manual counting method.
Quantifizierung aviärer Plasmodien mittels KI
Project leader
Himmel Tanja
Hochschuljubiläumsfonds der Stadt Wien
Type of Research
Applied research
Vetmed Research Units
Institute of Pathology
Funded by
Stadt Wien, Wien, Austria
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