Abstract
BackgroundRoutine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity. In addition, differential diagnosis of Plasmodium species has a high level of misdiagnosis. Real time remote microscopical diagnosis through on-line crowdsourcing platforms could be converted into an agile network to support diagnosis-based treatment and malaria control in low resources areas. This study explores whether accurate Plasmodium species identification—a critical step during the diagnosis protocol in order to choose the appropriate medication—is possible through the information provided by non-trained on-line volunteers.Methods88 volunteers have performed a series of questionnaires over 110 images to differentiate species (Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, Plasmodium malariae, Plasmodium knowlesi) and parasite staging from thin blood smear images digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Visual cues evaluated in the surveys include texture and colour, parasite shape and red blood size.ResultsOn-line volunteers are able to discriminate Plasmodium species (P. falciparum, P. malariae, P. vivax, P. ovale, P. knowlesi) and stages in thin-blood smears according to visual cues observed on digitalized images of parasitized red blood cells. Friendly textual descriptions of the visual cues and specialized malaria terminology is key for volunteers learning and efficiency.ConclusionsOn-line volunteers with short-training are able to differentiate malaria parasite species and parasite stages from digitalized thin smears based on simple visual cues (shape, size, texture and colour). While the accuracy of a single on-line expert is far from perfect, a single parasite classification obtained by combining the opinions of multiple on-line volunteers over the same smear, could improve accuracy and reliability of Plasmodium species identification in remote malaria diagnosis.
Highlights
Routine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity
Gametocytes were significantly differentiated from young trophozoites (P < 0.01) but not from mature trophozoites and schizonts
The present study explored the possibility of use the power of non-expert on-line volunteers to differentiate the five human Plasmodium species from samples of thin blood film digitalized by a smartphone coupled to a conventional light microscopy by an inexpensive way using a combination of microscopy and social networks
Summary
Routine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity. Real time remote microscopical diagnosis through on-line crowdsourc‐ ing platforms could be converted into an agile network to support diagnosis-based treatment and malaria control in low resources areas. This study explores whether accurate Plasmodium species identification—a critical step during the diagnosis protocol in order to choose the appropriate medication—is possible through the information provided by non-trained on-line volunteers. An accurate classification of the species at the time of diagnosis is crucial for directing people to an appropriate treatment, preventing over-treatment and injudicious use of anti-malarials, especially in co-endemic areas [2, 6,7,8]. Malaria species identification is important when different species have separate treatment policies, most commonly artemisinin-based combination therapy (ACT) for P. falciparum (and P. vivax at regions where chloroquine resistance is prevalent) and chloroquine for non-falciparum species. In areas which are endemic for P. knowlesi, an accurate diagnosis is important to reach an epidemiological surveillance of this potentially fatal emerging zoonotic infection [2, 9]
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