Identification and quantification of pathogenic helminth eggs using a digital image system

Abstract

A system was developed to identify and quantify up to seven species of helminth eggs (Ascaris lumbricoides -fertile and unfertile eggs-, Trichuris trichiura, Toxocara canis, Taenia saginata, Hymenolepis nana, Hymenolepis diminuta, and Schistosoma mansoni) in wastewater using different image processing tools and pattern recognition algorithms. The system was developed in three stages. Version one was used to explore the viability of the concept of identifying helminth eggs through an image processing system, while versions 2 and 3 were used to improve its efficiency. The system development was based on the analysis of different properties of helminth eggs in order to discriminate them from other objects in samples processed using the conventional United States Environmental Protection Agency (US EPA) technique to quantify helminth eggs. The system was tested, in its three stages, considering two parameters: specificity (capacity to discriminate between species of helminth eggs and other objects) and sensitivity (capacity to correctly classify and identify the different species of helminth eggs). The final version showed a specificity of 99% while the sensitivity varied between 80 and 90%, depending on the total suspended solids content of the wastewater samples. To achieve such values in samples with total suspended solids (TSS) above 150 mg/L, it is recommended to dilute the concentrated sediment just before taking the images under the microscope. The system allows the helminth eggs most commonly found in wastewater to be reliably and uniformly detected and quantified. In addition, it provides the total number of eggs as well as the individual number by species, and for Ascaris lumbricoides it differentiates whether or not the egg is fertile. The system only requires basically trained technicians to prepare the samples, as for visual identification there is no need for highly trained personnel. The time required to analyze each image is less than a minute. This system could be used in central analytical laboratories providing a remote analysis service.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

“…Different software solutions have been proposed for the automated identification and quantification of parasites, such as soil-transmitted helminths, in digitized samples with various levels of reported sensitivity and specificity [31,32]. Deep learning-based solutions to this type of pattern recognition tasks represent the state of the art in machine learning, and have recently attained significant attention due to the high performance of such algorithms in various image classification tasks [33–35].…”

Pathogens, faecal indicators and human-specific microbial source-tracking markers in sewage

“…Following concentration and enrichment, multicellular parasites and protozoa are routinely detected by microscopic observation or particle detection. Regular microscopy is the preferred method for detecting multicellular parasites for which a digital imaging system to identify and quantify several species of helminth eggs has recently been developed (Maya et al 2006;Jim enez et al 2016). Advanced methods such as immunofluorescence microscopy (Rose et al 1989), flow cytometry (Vesey et al 1994) or laser scanning cytometry (Montemayor et al 2007) are preferred for protozoa.…”

Trichuris trichiura

“…Unfortunately, DNA techniques do not substantially improve the sensitivity of the traditional microscopic techniques mainly because of the in the preservation of samples and DNA extraction. More recently, innovative techniques have been proposed for ova detection including the use of a microfluidic chambers to isolate ova (Izurieta and Selvaganapathy, 2016) as well as the use of an analytical digital image system to identify ova from wastewater samples processed using the US EPA technique (Jimenez et al, 2016). This analytical digital image system is reported to have a 99% specificity with a 80-90% sensitivity (Jimenez et al, 2016).…”

“…More recently, innovative techniques have been proposed for ova detection including the use of a microfluidic chambers to isolate ova (Izurieta and Selvaganapathy, 2016) as well as the use of an analytical digital image system to identify ova from wastewater samples processed using the US EPA technique (Jimenez et al, 2016). This analytical digital image system is reported to have a 99% specificity with a 80-90% sensitivity (Jimenez et al, 2016). Finally, the use of low cytometry or droplet digital PCR (ddPCR) for the analysis, detection and/or identification of ova in environmental samples has been proposed although yet to be reported (Amoah et al, 2017).…”

“…Wastewater has been widely reused for agricultural purposes for a long period of time, especially in developing nations where the need for irrigation surpasses water supply leading to reuse of low quality (i.e. inappropriately treated) wastewater (Maya et al, 2010;Jimenez et al, 2016). Ova concentration in wastewater has been reported to range from 1 (in the United States) to 840 (in Morocco) per liter whereas helminth ova concentration has been reported to range from <1 (in Germany) to 735 (in Egypt) per gram of total solids in sludge (Table 2) (Jimenez, 2007).…”