In most parasitic cycles, the free phase passes through an egg stage, which is released by the host into the environment via a complex faecal matrix, which has highly variable and often low egg concentrations. The classical detection method relies on microscopic observation of these eggs, which implies a tedious and time-consuming preparation of the sample to concentrate the eggs, with highly variable sensitivity values. This detection is crucial because, once dispersed, the eggs contaminate the environment and food, leading to cases of parasitic zoonoses in humans.
Detection in environmental and food matrices is even more complex than for faeces because of the very low number of eggs present : 1 to 10 per sample in the vast majority of cases. The thesis aims at developing a lensless wide-field imaging system that will allow the counting and identification of parasite eggs in complex matrices, while increasing sensitivity. This will make it possible to automate detection, thus opening up the possibility of investigating more samples for better health surveillance.