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Int J Parasitol. 2016 Jul;46(8):485-93. doi: 10.1016/j.ijpara.2016.02.004. Epub 2016 Mar 26.

Automated parasite faecal egg counting using fluorescence labelling, smartphone image capture and computational image analysis.

Author information

1
MEP Equine Solutions, 3905 English Oak Circle, Lexington, KY 40514, USA; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA. Electronic address: pslusarewicz@mepequinesolutions.com.
2
MEP Equine Solutions, 3905 English Oak Circle, Lexington, KY 40514, USA; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
3
Department of Molecular & Cellular Biochemistry, University of Kentucky, Lexington, KY, USA.
4
M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.

Abstract

Intestinal parasites are a concern in veterinary medicine worldwide and for human health in the developing world. Infections are identified by microscopic visualisation of parasite eggs in faeces, which is time-consuming, requires technical expertise and is impractical for use on-site. For these reasons, recommendations for parasite surveillance are not widely adopted and parasite control is based on administration of rote prophylactic treatments with anthelmintic drugs. This approach is known to promote anthelmintic resistance, so there is a pronounced need for a convenient egg counting assay to promote good clinical practice. Using a fluorescent chitin-binding protein, we show that this structural carbohydrate is present and accessible in shells of ova of strongyle, ascarid, trichurid and coccidian parasites. Furthermore, we show that a cellular smartphone can be used as an inexpensive device to image fluorescent eggs and, by harnessing the computational power of the phone, to perform image analysis to count the eggs. Strongyle egg counts generated by the smartphone system had a significant linear correlation with manual McMaster counts (R(2)=0.98), but with a significantly lower coefficient of variation (P=0.0177). Furthermore, the system was capable of differentiating equine strongyle and ascarid eggs similar to the McMaster method, but with significantly lower coefficients of variation (P<0.0001). This demonstrates the feasibility of a simple, automated on-site test to detect and/or enumerate parasite eggs in mammalian faeces without the need for a laboratory microscope, and highlights the potential of smartphones as relatively sophisticated, inexpensive and portable medical diagnostic devices.

KEYWORDS:

Ascarid; Chitin; Egg count; Equine; Fluorescence; Image analysis; Smartphone; Strongyle

PMID:
27025771
DOI:
10.1016/j.ijpara.2016.02.004
[Indexed for MEDLINE]

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