The online version of this article (https://doi.org/10.1186/s12936-018-2194-8) contains supplementary material, which is available to authorized users.
Miguel Luengo-Oroz and María Linares contributed equally to this work
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. 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.
88 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.
On-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.
On-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.
Additional file 1. Example of supporting example images used in the query. (a) : young trophozoites, (b) mature trophozoites, (c) schizonts and (d) gametocytes of Plasmodium knowlesi. (e) Percentage of correct answers when support images were used (+) or not (−). Values given are the mean ± SEM calculated for the different images shown in a total of 32 volunteers. Asterisk indicates a significant difference between the percentage of response of the correct answer and each other two possibilities. * P < 0.05.
WHO. World malaria report. Geneva: World Health Organization; 2016. http://www.who.int/malaria/publications/world-malaria-report-2016/report/en/. Accessed 1 Jan 2017.
WHO. World malaria report. Geneva: World Health Organization; 2015. http://www.who.int/malaria/publications/world-malaria-report-2015/en/. Accessed 6 Jun 2016.
WHO. Methods for field trials of malaria rapid diagnostic tests. Geneva: World Health Organization; 2009. http://www.who.int/malaria/publications/atoz/9789290614166_field_trials/en/. Accessed 11 Jan 2017.
Kaewkamnerd S, Uthaipibull C, Intarapanich A, Pannarut M, Chaotheing S, Tongsima S. An automatic device for detection and classification of malaria parasite species in thick blood film. BMC Bioinform. 2012;13:S18. CrossRef
WHO. Guidelines for the treatment of malaria. Geneva: World Health Organization; 2015. http://www.who.int/malaria/publications/atoz/9789241549127/en/. Accessed 9 Jan 2017.
Andrade BB, Reis-Filho A, Barros AM, Souza-Neto SM, Nogueira LL, Fukutani KF, et al. Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks. Malar J. 2010;9:117. CrossRefPubMedPubMedCentral
Tek FB, Dempster AG, Kale I. Malaria parasite detection in peripheral blood images. In: Proceedings British Machine Vision Conference, Manchester. 2006. p. 47–56.
Schenkmans L. All hands on deck. Science. 2010;330:431.
Albers L. Gamers join real-life fight against malaria and tuberculosis. Lancet Infect Dis. 2016;16:418. CrossRef
Feng S, Woo M-J, Kim H, Kim E, Ki S, Shao L, Ozcan A. A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis. In: Proc SPIE optics and biophotonics in low-resource settings II. 2016; https://doi.org/10.1117/12.2212310.
Servonnet A, Rapp C, Delacour H, Bigaillon C, Pilo JE, Merens A. Plasmodium knowlesi: une espèce émergente chez l’homme? Médecine et Santé Tropicales. 2012;22:417–21. PubMed
Mohapatra PK, Prakash A, Bhattacharyya DR, Goswami BK, Ahmed A, Sarmah B, et al. Detection & molecular confirmation of a focus of Plasmodium malariae in Arunachal Pradesh, India. Indian J Med Res. 2008;128:52–6. PubMed
CDC. Comparison of Plasmodium species which cause malaria in humans. Centers for Disease Control and Prevention; 2013. https://www.cdc.gov/dpdx/malaria/dx.html. Accessed 1 May 2017.
- Plasmodium species differentiation by non-expert on-line volunteers for remote malaria field diagnosis
José M. Bautista
José Miguel Rubio
- BioMed Central
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