Last Updated: 07/11/2024
Smartphone Malaria (SMMALA)
Objectives
The objective of the study is to increase the performance of current algorithm, then to implement the tool in a smartphone application which will be tested and validated in the field in Benin.
In the current perspective of global elimination of malaria, the search for more sensitive diagnostic tools is a key element in the fight against the disease. Alternative automated microscopic diagnostic techniques, based on deep learning image analysis algorithms are emerging, and a recent promising avenue is the development of diagnostic applications on smartphones. However, current solutions achieve a tool sensitivity equivalent to that achieved by an experimented microscopist. The original idea of this study is also based on a deep learning approach, but here this proposes a new learning paradigm allowing to considerably increase the sensitivity of the tool. Preliminary funding allowed the establishment of the proof of concept by testing the hypothesis on field data from Bénin.
Oct 2023 — Oct 2026
$437,751
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