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

Evaluation of Parasight All-in-One system for the automated enumeration of helminth ova in canine and feline feces

BACKGROUND

 Digital imaging combined with deep-learning-based computational image analysis is a growing area in medical diagnostics, including parasitology, where a number of automated analytical devices have been developed and are available for use in clinical practice.

METHODS

The performance of Parasight All-in-One (AIO), a second-generation device, was evaluated by comparing it to a well-accepted research method (mini-FLOTAC) and to another commercially available test (Imagyst). Fifty-nine canine and feline infected fecal specimens were quantitatively analyzed by all three methods. Since some samples were positive for more than one parasite, the dataset consisted of 48 specimens positive for Ancylostoma spp., 13 for Toxocara spp. and 23 for Trichuris spp.

RESULTS

The magnitude of Parasight AIO counts correlated well with those of mini-FLOTAC but not with those of Imagyst. Parasight AIO counted approximately 3.5-fold more ova of Ancylostoma spp. and Trichuris spp. and 4.6-fold more ova of Toxocara spp. than the mini-FLOTAC, and counted 27.9-, 17.1- and 10.2-fold more of these same ova than Imagyst, respectively. These differences translated into differences between the test sensitivities at low egg count levels (< 50 eggs/g), with Parasight AIO > mini-FLOTAC > Imagyst. At higher egg counts Parasight AIO and mini-FLOTAC performed with comparable precision (which was significantly higher that than Imagyst), whereas at lower counts (> 30 eggs/g) Parasight was more precise than both mini-FLOTAC and Imagyst, while the latter two methods did not significantly differ from each other.

CONCLUSIONS

In general, Parasight AIO analyses were both more precise and sensitive than mini-FLOTAC and Imagyst and quantitatively correlated well with mini-FLOTAC. While Parasight AIO produced lower raw counts in eggs-per-gram than mini-FLOTAC, these could be corrected using the data generated from these correlations.

BEVA primary care clinical guidelines: Equine parasite control

BACKGROUND

There is a lack of consensus on how best to balance our need to minimise the risk of parasite-associated disease in the individual horse, with the need to limit the use of anthelmintics in the population to preserve their efficacy through delaying further development of resistance.

OBJECTIVES

To develop evidence-based guidelines utilising a modified GRADE framework.

METHODS

A panel of veterinary scientists with relevant expertise and experience was convened. Relevant research questions were identified and developed with associated search terms being defined. Evidence in the veterinary literature was evaluated using the GRADE evidence-to-decision framework. Literature searches were performed utilising CAB abstracts and PubMed. Where there was insufficient evidence to answer the research question the panel developed practical guidance based on their collective knowledge and experience.

RESULTS

Search results are presented, and recommendation or practical guidance were made in response to 37 clinically relevant questions relating to the use of anthelmintics in horses.

MAIN LIMITATIONS

There was insufficient evidence to answer many of the questions with any degree of certainty and practical guidance frequently had to be based upon extrapolation of relevant information and the panel members' collective experience and opinions.

CONCLUSIONS

Equine parasite control practices and current recommendations have a weak evidence base. These guidelines highlight changes in equine parasite control that should be considered to reduce the threat of parasite-associated disease and delay the development of further anthelmintic resistance.

US-wide equine strongylid egg count data demonstrate seasonal and regional trends

Equine strongylid parasites are ubiquitous around the world and are main targets of parasite control programmes. In recent years, automated fecal egg counting systems based on image analysis have become available allowing for collection and analysis of large-scale egg count data. This study aimed to evaluate equine strongylid fecal egg count (FEC) data generated with an automated system over three years in the US with specific attention to seasonal and regional trends in egg count magnitude and sampling activity. Five US regions were defined; North East, South East, North Central, South Central and West. The data set included state, region and zip code for each FEC. The number of FECs falling in each of the following categories were recorded: (1) 0 eggs per gram (EPG), (2) 1 ⩽ 200 EPG, (3) 201 ⩽ 500 EPG and (4) >500 EPG. The data included 58 329 FECs. A fixed effects model was constructed fitting the number of samples analysed per month, year and region, and a mixed effects model was constructed to fit the number of FECs falling in each of the 4 egg count categories defined above. The overall proportion of horses responsible for 80% of the total FEC output was 18.1%, and this was consistent across years, months and all regions except West, where the proportion was closer to 12%. Statistical analyses showed significant seasonal trends and regional differences of sampling frequency and FEC category. The data demonstrated that veterinarians tended to follow a biphasic pattern when monitoring strongylid FECs in horses, regardless of location.

Relative egg extraction efficiencies of manual and automated fecal egg count methods in equines

The World Association for the Advancement of Veterinary Parasitology recently released new recommendations for the design of fecal egg count (FEC) reduction tests for livestock. These provide suggestions as to the number of animals to be sampled and the minimum number of eggs that must be counted to produce statistically meaningful results. One of the considerations for study design is the multiplication factor of the FEC method to be used; methods with lower multiplication factors require fewer animals to be sampled because they are presumed to count more eggs per test. However, multiplication factor is not the sole determinant of the number of eggs counted by any given method, since different techniques use very different sample extraction methodologies that could affect the number of eggs detected beyond just the amount of feces examined. In this light, we compared three commonly used manual FEC methods (mini-FLOTAC, McMaster and Wisconsin) and two automated methods (Imagyst and Parasight All-in-One) with respect to how many equine strongylid and ascarid eggs they counted in the same samples. McMaster and mini-FLOTAC (multiplication factors of 25x and 5x, respectively) produced the most accurate results of the methods tested but mini-FLOTAC counted approximately 5-times more eggs than McMaster. However, Wisconsin and Parasight (multiplication factor = 1x) counted 3-times more ova than mini-FLOTAC, which was less than the 5-fold difference in their multiplication factors. As a result, these tests perform with multiplication factors more akin to 1.6x relative to mini-FLOTAC. Imagyst, due to its unique sample preparation methodology, does not have a traditional multiplication factor but performed similarly to McMaster with respect to egg recovery.

Rapid, automated quantification of Haemonchus contortus ova in sheep faecal samples

Haemonchus contortus is one of the most pathogenic nematodes affecting small ruminants globally and is responsible for large economic losses in the sheep and goat industry. Anthelmintic resistance is rampant in this parasite and thus parasite control programs must account for drug efficacy on individual farms and, sometimes, whether H. contortus is the most prevalent trichostrongylid. Historically, coproculture has been the main way to determine the prevalence of H. contortus in faecal samples due to the inability to morphologically differentiate between trichostrongylid egg types, but this process requires a skilled technician and takes multiple days to complete. Fluoresceinated peanut agglutinin (PNA) has been shown to specifically bind H. contortus and thus differentiate eggs based on whether they fluoresce, but this method has not been widely adopted. The ParasightTM System (PS) fluorescently stains helminth eggs in order to identify and quantify them, and the H. contortus PNA staining method was therefore adapted to this platform using methodology requiring only 20 min to obtain results. In this study, 74 fecal samples were collected from sheep and analyzed for PNA-stained H. contortus, using both PS and manual fluorescence microscopy. The percentage of H. contortus was determined based on standard total strongylid counts with PS or brightfield microscopy. Additionally, 15 samples were processed for coproculture with larval identification, and analyzed with both manual and automated PNA methods. All methods were compared using the coefficient of determination (R2) and the Lin's concordance correlation coefficient (ρc). ParasightTM and manual PNA percent H. contortus results were highly correlated with R2 = 0.8436 and ρc = 0.9100 for all 74 fecal samples. Coproculture versus PS percent H. contortus were also highly correlated with R2 = 0.8245 and ρc = 0.8605. Overall, this system provides a rapid and convenient method for determining the percentage of H. contortus in sheep and goat fecal samples without requiring specialized training.

Multicenter evaluation of the Vetscan Imagyst system using Ocus 40 and EasyScan One scanners to detect gastrointestinal parasites in feces of dogs and cats

The Vetscan Imagyst system (Zoetis) is a novel, artificial intelligence-driven detection tool that can assist veterinarians in the identification of enteric parasites in dogs and cats. This system consists of a sample preparation device, an automated digital microscope scanner, and a deep-learning algorithm. The EasyScan One scanner (Motic) has had good diagnostic performance compared with manual examinations by experts; however, there are drawbacks when used in veterinary practices in which space for equipment is often limited. To improve the usability of this system, we evaluated an additional scanner, the Ocus 40 (Grundium). Our objectives were to 1) qualitatively evaluate the performance of the Vetscan Imagyst system with the Ocus 40 scanner for identifying Ancylostoma, Toxocara, and Trichuris eggs, Cystoisospora oocysts, and Giardia cysts in canine and feline fecal samples, and 2) expand the assessment of the performance of the Vetscan Imagyst system paired with either the Ocus 40 or EasyScan One scanner to include a larger dataset of 2,191 fecal samples obtained from 4 geographic regions of the United States. When tested with 852 canine and feline fecal samples collected from different geographic regions, the performance of the Vetscan Imagyst system combined with the Ocus 40 scanner was correlated closely with manual evaluations by experts. Sensitivities were 80.0‒97.0% and specificities were 93.7‒100.0% across the targeted parasites. When tested with 1,339 fecal samples, the Vetscan Imagyst system paired with the EasyScan One scanner successfully identified the targeted parasite stages; sensitivities were 73.6‒96.4% and specificities were 79.7‒100.0%.

Parascaris spp. eggs shedding patterns in juvenile horses

Parascaris spp. infect foals worldwide and foals typically shed eggs in the feces from about three to six months of age, upon which natural immunity is incurred. High levels of anthelmintic resistance of Parascaris spp. are a global concern, and further understanding egg shedding patterns and fecal egg counting (FEC) data variability is of high importance. The aims of this study were to monitor Parascaris spp. egg shedding in untreated foals during 12-23 weeks of age, estimate sources of data variability, and assess precision of two ascarid FEC techniques. Fecal samples were collected weekly from 11 foals born in 2022, from May through November (29 weeks). Six subsamples were extracted from each weekly sample to determine 30 FECs between two techniques: a McMaster technique and an Automated Egg Counting System (AECS). Mixed linear modeling was carried out with age, sex, birth month, seasonality, spring- or summer-born foals, and egg counting technique as explanatory variables. Ascarid FECs were associated with age (p < 0.001), seasonality (p < 0.001), and technique (p < 0.001). The McMaster technique was more precise with a mean coefficient of variation (CV) of 34.57% and a 95% confidence interval (CI) of 30.80%- 38.30% compared to the CV for the AECS, which was 42.22% (CI: 37.70%-46.70%). Seasonality accounted for the highest proportion of variance (PV) of all covariates, but differences in PVs for covariates existed between techniques with foal age and subsample contributing more variance to the McMaster, and individual foal and seasonality contributing more to the AECS. Subsamples and replicate counts accounted for less than 1% of the total data variance. The results highlighted substantial differences in PVs between the two techniques at the subsample (AECS: 57.14%; McMaster: 77.51%) and replicate count levels (AECS: 42.86%; McMaster: 22.49%). While differences in precision were observed between the two FEC techniques, they were negligible in the data set, as the overwhelming majority of the data variability in ascarid FECs was attributed to individual foal, seasonality, and foal age.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Are ChatGPT and other pretrained language models good parasitologists?

Large language models, such as ChatGPT, will have far-reaching impacts on parasitology, including on students. Authentic experiences gained during students' training are absent from these models. This is not a weakness of the models but rather an opportunity benefiting parasitology at large.

Molecular diagnostics for gastrointestinal helminths in equids: Past, present and future

This review is aimed to (i) appraise the literature on the use of molecular techniques for the detection, quantification and differentiation of gastrointestinal helminths (GIH) of equids, (ii) identify the knowledge gaps and, (iii) discuss diagnostic prospects in equine parasitology. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews, we retrieved 54 studies (horses: 50/54; donkeys and zebras: 4/54) from four databases. Polymerase chain reaction (PCR) was employed in all of the studies whereas PCR amplicons were sequenced in only 18 of them. Other techniques used (including modifications of PCR) were reverse line blot, quantitative (q)PCR, restriction fragment length polymorphism, nested-PCR, PCR-directed next-generation sequencing, Southern blotting, single strand conformation polymorphism, PCR-enzyme linked immunosorbent assay, matrix-assisted laser desorption/ionisation-time of flight and random amplification of polymorphic DNA. Most of the studies (53/54) used nuclear ribosomal RNA (including the internal transcribed spacers, intergenic spacer, 5.8 S, 18 S, 28 S and 12 S) as target loci while cytochrome c oxidase subunit 1 and random genomic regions were targeted in only three and one studies, respectively. Overall, to date, the majority of molecular studies have focused on the diagnosis and identification of GIHs of equids (i.e. species of Anoplocephala, Craterostomum, cyathostomins, Oesophagodontus, Parascaris, Strongylus, Strongyloides and Triodontophorus), with a recent shift towards investigations on anthelmintic resistance and the use of high-throughput nemabiome metabarcoding. With the increasing reports of anthelmintic resistance in equid GIHs, it is crucial to develop and apply techniques such as advanced metabarcoding for surveillance of parasite populations in order to gain detailed insights into their diversity and sustainable control. To the best of our knowledge, this is the first systematic review that evaluates molecular investigations published on the diagnosis and quantification of equid GIHs and provides useful insights into important knowledge gaps and future research directions in equid molecular parasitology.

Advances in diagnosis of gastrointestinal nematodes in livestock and companion animals

Diagnosis of gastrointestinal nematodes in livestock and companion animals has been neglected for years and there has been an historical underinvestment in the development and improvement of diagnostic tools, undermining the undoubted utility of surveillance and control programmes. However, a new impetus by the scientific community and the quickening pace of technological innovations, are promoting a renaissance of interest in developing diagnostic capacity for nematode infections in veterinary parasitology. A cross-cutting priority for diagnostic tools is the development of pen-side tests and associated decision support tools that rapidly inform on the levels of infection and morbidity. This includes development of scalable, parasite detection using artificial intelligence for automated counting of parasitic elements and research towards establishing biomarkers using innovative molecular and proteomic methods. The aim of this review is to assess the state-of-the-art in the diagnosis of helminth infections in livestock and companion animals and presents the current advances of diagnostic methods for intestinal parasites harnessing (i) automated methods for copromicroscopy based on artificial intelligence, (ii) immunodiagnosis, and (iii) molecular- and proteome-based approaches. Regardless of the method used, multiple factors need to be considered before diagnostics test results can be interpreted in terms of control decisions. Guidelines on how to apply diagnostics and how to interpret test results in different animal species are increasingly requested and some were recently made available in veterinary parasitology for the different domestic species.

Comparison of FECPAKG2, a modified Mini-FLOTAC technique and combined sedimentation and flotation for the coproscopic examination of helminth eggs in horses

Background

Due to high prevalence of anthelmintic resistance in equine helminths, selective treatment is increasingly promoted and in some countries a positive infection diagnosis is mandatory before treatment. Selective treatment is typically recommended when the number of worm eggs per gram faeces (epg) exceeds a particular threshold. In the present study we compared the semi-quantitative sedimentation/flotation method with the quantitative methods Mini-FLOTAC and FECPAK^G2 in terms of precision, sensitivity, inter-rater reliability and correlation of worm egg counts to improve the choice of optimal diagnostic tools.

Methods

Using sedimentation/flotation (counting raw egg numbers up to 200), we investigated 1067 horse faecal samples using a modified Mini-FLOTAC approach (multiplication factor of 5 to calculate epgs from raw egg counts) and FECPAK^G2 (multiplication factor of 45).

Results

Five independent analyses of the same faecal sample with all three methods revealed that variance was highest for the sedimentation/flotation method while there were no significant differences between methods regarding the coefficient of variance. Sedimentation/flotation detected the highest number of samples positive for strongyle and Parascaris spp. eggs, followed by Mini-FLOTAC and FECPAK^G2. Regarding Anoplocephalidae, no significant difference in frequency of positive samples was observed between Mini-FLOTAC and sedimentation/flotation. Cohen’s κ values comparing individual methods with the combined result of all three methods revealed almost perfect agreement (κ ≥ 0.94) for sedimentation/flotation and strong agreement for Mini-FLOTAC (κ ≥ 0.83) for strongyles and Parascaris spp. For FECPAK^G2, moderate and weak agreements were found for the detection of strongyle (κ = 0.62) and Parascaris (κ = 0.51) eggs, respectively. Despite higher sensitivity, the Mini-FLOTAC mean epg was significantly lower than that with FECPAK^G2 due to samples with > 200 raw egg counts by sedimentation/flotation, while in samples with lower egg shedding epgs were higher with Mini-FLOTAC than with FECPAK^G2.

Conclusions

For the simple detection of parasite eggs, for example, to treat foals infected with Parascaris spp., sedimentation/flotation is sufficient and more sensitive than the other two quantitative investigared in this study. Mini-FLOTAC is predicted to deliver more precise results in faecal egg count reduction tests due to higher raw egg counts. Finally, to identify animals with a strongyle epg above a certain threshold for treatment, FECPAK^G2 delivered results comparable to Mini-FLOTAC.

Grpahical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05266-y.

A Comprehensive Method for the Evaluation of Hermetia illucens Egg Quality Parameters: Implications and Influence Factors

The significant momentum received by Hermetia illucens as a worldwide species is due to its biological traits and large applicability in scientific research, environmental entomoremediation, insect meal production, and for biodiesel yield. The aim of this research is to develop a method for the preparation and precise egg counting of the H. illucens egg clutch, as well as an accurate technique for evaluating egg biometric parameters. The precise proposed method for egg preparation and counting consists in dispersing the eggs clutch under a stereo microscope and counting the eggs on a photographic capture using the Clickmaster software. Five solution types were used to disperse the egg clutches: glycerin 50%, ethanol 70%, ethanol 80%, physiological serum 0.9% and purified water. The efficiency of the estimation method for eggs number evaluation was also tested by using the estimated egg weight as a conversion factor. The biometric parameters of single eggs (length and width) were determined using the free Toupview software. The precise method of egg preparation and counting allows for the registration of the eggs number manually identified by the operator. The appropriate dispersion solutions were glycerin 50% and ethanol 70%. The method has an error of 1.4 eggs for each 500 counted eggs, thus an accuracy of over 99.4%. The eggs number estimation method is not applicable without significant errors, the accuracy being less than 32%, due to egg heterogeneity in the clutch. Biometric parameters (length and width) are positively correlated with egg weight (r = 0.759) and with the number of eggs in the clutch (r = 0.645). In conclusion, the results clearly suggest the method of egg preparation and precise counting for an accurate evaluation of quality parameters of the H. illucens clutches, as well as the technique for evaluating egg biometric parameters.

Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network

Ancylostomiasis is a fairly common small bowel parasite disease identified by capsule endoscopy (CE) for which a computer-aided clinical detection method has not been established. We sought to develop an artificial intelligence system with a convolutional neural network (CNN) to automatically detect hookworms in CE images. We trained a deep CNN system based on a YOLO-V4 (You Look Only Once-Version4) detector using 11236 CE images of hookworms. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,529 small-bowel images including 531 images of hookworms. The trained CNN system required 403 seconds to evaluate 10,529 test images. The area under the curve for the detection of hookworms was 0.972 (95% confidence interval (CI), 0.967-0.978). The sensitivity, specificity, and accuracy of the CNN system were 92.2%, 91.1%, and 91.2%, respectively, at a probability score cut-off of 0.485. We developed and validated a CNN-based system for detecting hookworms in CE images. By combining this high-accuracy, high-speed, and oversight-preventing system with other CNN systems, we hope it will become an important supplement for detecting intestinal abnormalities in CE images. This trial is registered with ChiCTR2000034546 (a clinical research of artificial-intelligence-aided diagnosis for hookworms in small intestine by capsule endoscope images).

Effects of sample homogenizing on the performance of an automated strongylid egg counting system

Fecal egg counts are essential monitoring tools in veterinary parasite control. In recent years, several groups have developed automated egg counting systems based on image analysis and deep learning algorithms. Work in our laboratory demonstrated that an automated system performed with significantly better precision than traditional egg counting techniques. However, while the counting process is no longer operator dependent, the pre-analytical homogenization steps still are. This study aimed at evaluating the influence of sample homogenization on diagnostic performance on an automated equine strongylid egg counting system. Samples were collected from 12 horses and assigned to three egg count categories (four samples per category): Low (0-500 eggs per gram (EPG)), Moderate (501-1000 EPG), and High (1001-2000 EPG). Within each category, all samples were divided into four portions and each was analyzed with the automated system using the following four homogenizing procedures using a homogenizing device supplied with the system: 1) pressing the plunger five times and pouring directly into the counting chamber, 2) pressing the plunger five times and shaking the bottle prior to pouring, 3) pressing the plunger ten times with direct pouring, and 4) pressing the plunger ten times with shaking the bottle before pouring. There were no differences in precision expressed as coefficient of variation between these four procedures but shaking of the bottle prior to pouring was significantly associated with higher counts (p = 0.0068). These results demonstrate that the homogenization process can affect the diagnostic performance of an automated egg counting system and suggest that more efforts should be invested in standardizing and optimizing homogenization procedures.

Integrative biology defines novel biomarkers of resistance to strongylid infection in horses

The widespread failure of anthelmintic drugs against nematodes of veterinary interest requires novel control strategies. Selective treatment of the most susceptible individuals could reduce drug selection pressure but requires appropriate biomarkers of the intrinsic susceptibility potential. To date, this has been missing in livestock species. Here, we selected Welsh ponies with divergent intrinsic susceptibility (measured by their egg excretion levels) to cyathostomin infection and found that their divergence was sustained across a 10-year time window. Using this unique set of individuals, we monitored variations in their blood cell populations, plasma metabolites and faecal microbiota over a grazing season to isolate core differences between their respective responses under worm-free or natural infection conditions. Our analyses identified the concomitant rise in plasma phenylalanine level and faecal Prevotella abundance and the reduction in circulating monocyte counts as biomarkers of the need for drug treatment (egg excretion above 200 eggs/g). This biological signal was replicated in other independent populations. We also unravelled an immunometabolic network encompassing plasma beta-hydroxybutyrate level, short-chain fatty acid producing bacteria and circulating neutrophils that forms the discriminant baseline between susceptible and resistant individuals. Altogether our observations open new perspectives on the susceptibility of equids to strongylid infection and leave scope for both new biomarkers of infection and nutritional intervention.

Systematic Review and Meta-Analysis of the Diagnostic Accuracy of Mobile-Linked Point-of-Care Diagnostics in Sub-Saharan Africa

Mobile health devices are emerging applications that could help deliver point-of-care (POC) diagnosis, particularly in settings with limited laboratory infrastructure, such as Sub-Saharan Africa (SSA). The advent of Severe acute respiratory syndrome coronavirus 2 has resulted in an increased deployment and use of mHealth-linked POC diagnostics in SSA. We performed a systematic review and meta-analysis to evaluate the accuracy of mobile-linked point-of-care diagnostics in SSA. Our systematic review and meta-analysis were guided by the Preferred Reporting Items requirements for Systematic Reviews and Meta-Analysis. We exhaustively searched PubMed, Science Direct, Google Scholar, MEDLINE, and CINAHL with full text via EBSCOhost databases, from mHealth inception to March 2021. The statistical analyses were conducted using OpenMeta-Analyst software. All 11 included studies were considered for the meta-analysis. The included studies focused on malaria infections, Schistosoma haematobium, Schistosoma mansoni, soil-transmitted helminths, and Trichuris trichiura. The pooled summary of sensitivity and specificity estimates were moderate compared to those of the reference representing the gold standard. The overall pooled estimates of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of mobile-linked POC diagnostic devices were as follows: 0.499 (95% CI: 0.458–0.541), 0.535 (95% CI: 0.401–0.663), 0.952 (95% CI: 0.60–1.324), 1.381 (95% CI: 0.391–4.879), and 0.944 (95% CI: 0.579–1.538), respectively. Evidence shows that the diagnostic accuracy of mobile-linked POC diagnostics in detecting infections in SSA is presently moderate. Future research is recommended to evaluate mHealth devices’ diagnostic potential using devices with excellent sensitivities and specificities for diagnosing diseases in this setting.

Development and performance of an automated fecal egg count system for small ruminant strongylids

An automated equine fecal egg count test, known as the Parasight System, was modified for use with small ruminants. Modifications included the introduction of a short centrifugation step in a floatation medium, an adjustment in pre-test sample filtering, and training of an image analysis-based egg counting algorithm to recognize and enumerate trichostrongylid eggs. In preliminary assessments, the modified method produced trichostrongylid egg counts comparable to manual McMaster analyses of the same samples from both ovine and caprine sources. The coefficient of determination (R²) for the linear correlation between McMaster and automated counts from these samples was 0.958, and there were no significant differences when comparing counts using feces from either sheep or goats. More extensive comparison utilized ovine samples split into three groups based on trichostrongylid egg content: Low (201-500 EPG), Medium (501-1000 EPG) and High (1001 or greater EPG). Each group contained 5 samples, each of which was used to produce individual slurries that were counted 8 times each using both McMaster and the automated method. This, again, showed no difference in accuracy between the techniques, but revealed significantly higher precision, as assessed by coefficients of variation (CoV), for the automated method for determining egg counts in the Low and Medium groups. The CoV of the McMaster method was 2.2, 2.5 and 1.3 times greater than the automated in the Low, Medium and High groups, respectively. Overall, the automated egg counting system showed good linear agreement with trichostrongylid egg counts determined with the McMaster method, and demonstrated significantly better precision. This technology reduces operator error and the results presented here illustrate its utility for determination of small ruminant trichostrongylid fecal egg counts.

The effect of analyst training on fecal egg counting variability

Fecal egg counts (FECs) are essential for veterinary parasite control programs. Recent advances led to the creation of an automated FEC system that performs with increased precision and reduces the need for training of analysts. However, the variability contributed by analysts has not been quantified for FEC methods, nor has the impact of training on analyst performance been quantified. In this study, three untrained analysts performed FECs on the same slides using the modified McMaster (MM), modified Wisconsin (MW), and the automated system with two different algorithms: particle shape analysis (PSA) and machine learning (ML). Samples were screened and separated into negative (no strongylid eggs seen), 1-200 eggs per gram of feces (EPG), 201-500 EPG, 501-1000 EPG, and 1001+ EPG levels, and ten repeated counts were performed for each level and method. Analysts were then formally trained and repeated the study protocol. Between analyst variability (BV), analyst precision (AP), and the proportion of variance contributed by analysts were calculated. Total BV was significantly lower for MM post-training (p = 0.0105). Additionally, AP variability and analyst variance both tended to decrease for the manual MM and MW methods. Overall, MM had the lowest BV both pre- and post-training, although PSA and ML were minimally affected by analyst training. This research illustrates not only how the automated methods could be useful when formal training is unavailable but also how impactful formal training is for traditional manual FEC methods.

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm

Background

Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST system was developed to provide simpler and easier fecal examinations which are less influenced by examiners’ skills. This system consists of three components: a sample preparation device, an automated microscope scanner, and analysis software. The objectives of this study were to qualitatively evaluate the performance of the VETSCAN IMAGYST system on feline parasites (Ancylostoma and Toxocara cati) and protozoan parasites (Cystoisospora and Giardia) and to assess and compare the performance of the VETSCAN IMAGYST centrifugal flotation method to reference centrifugal and passive flotation methods.

Methods

To evaluate the diagnostic performance of the scanning and algorithmic components of the VETSCAN IMAGYST system, fecal slides were prepared by the VETSCAN IMAGYST centrifugal flotation technique with pre-screened fecal samples collected from dogs and cats and examined by both an algorithm and parasitologists. To assess the performance of the VETSCAN IMAGYST centrifugal flotation technique, diagnostic sensitivity and specificity were calculated and compared to those of conventional flotation techniques.

Results

The performance of the VETSCAN IMAGYST algorithm closely correlated with evaluations by parasitologists, with sensitivity of 75.8–100% and specificity of 93.1-100% across the targeted parasites. For samples with 50 eggs or less per slide, Lin’s concordance correlation coefficients ranged from 0.70 to 0.95 across the targeted parasites. The results of the VETSCAN IMAGYST centrifugal flotation method correlated well with those of the conventional centrifugal flotation method across the targeted parasites: sensitivity of 65.7–100% and specificity of 97.6–100%. Similar results were observed for the conventional passive flotation method compared to the conventional centrifugal flotation method: sensitivity of 56.4–91.7% and specificity of 99.4–100%.

Conclusions

The VETSCAN IMAGYST scanning and algorithmic systems with the VETSCAN IMAGYST fecal preparation technique demonstrated a similar qualitative performance to the parasitologists’ examinations with conventional fecal flotation techniques. Given the deep learning nature of the VETSCAN IMAGYST system, its performance is expected to improve over time, enabling it to be utilized in veterinary clinics to perform fecal examinations accurately and efficiently.

Comparative studies on faecal egg counting techniques used for the detection of gastrointestinal parasites of equines: A systematic review

Faecal egg counting techniques (FECT) form the cornerstone for the detection of gastrointestinal parasites in equines. For this purpose, several flotation, centrifugation, image- and artificial intelligence-based techniques are used, with varying levels of performance. This review aimed to critically appraise the literature on the assessment and comparison of various coprological techniques and/or modifications of these techniques used for equines and to identify the knowledge gaps and future research directions. We searched three databases for published scientific studies on the assessment and comparison of FECT in equines and included 27 studies in the final synthesis. Overall, the performance parameters of McMaster (81.5%), Mini-FLOTAC® (33.3%) and simple flotation (25.5%) techniques were assessed in most of the studies, with 77.8% of them comparing the performance of at least two or three methods. The detection of strongyle, Parascaris spp. and cestode eggs was assessed for various FECT in 70.4%, 18.5% and 18.5% studies, respectively. A sugar-based flotation solution with a specific gravity of ≥1.2 was found to be the optimal flotation solution for parasitic eggs in the majority of FECT. No uniform or standardised protocol was followed for the comparison of various FECT, and the tested sample size (i.e. equine population and faecal samples) also varied substantially across all studies. To the best of our knowledge, this is the first systematic review to evaluate studies on the comparison of FECT in equines and it highlights important knowledge gaps in the evaluation and comparison of such techniques.

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An assessment of studies on the comparison of faecal egg counting in equine parasitology was undertaken.

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A consensus on the methodology and performance parameters for faecal egg counting techniques is required.

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Technical and biological sources of variability in faecal egg counts should be considered.

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Minimum analytical and diagnostic performance parameters should be assessed.

The Kubic FLOTAC microscope (KFM): a new compact digital microscope for helminth egg counts

The Kubic FLOTAC microscope (KFM) is a compact, low-cost, versatile and portable digital microscope designed to analyse fecal specimens prepared with Mini-FLOTAC or FLOTAC, in both field and laboratory settings. In this paper, we present the characteristics of the KFM along with its first validation for fecal egg count (FEC) of gastrointestinal nematodes (GINs) in cattle. For this latter purpose, a study was performed on 30 fecal samples from cattle experimentally infected by GINs to compare the performance of Mini-FLOTAC either using a traditional optical microscope (OM) or the KFM. The results of the comparison showed a substantial agreement (concordance correlation coefficient = 0.999), with a very low discrepancy (−0.425 ± 7.370) between the two microscopes. Moreover, the KFM captured images comparable with the view provided by the traditional OM. Therefore, the combination of sensitive, accurate, precise and standardized FEC techniques, as the Mini-FLOTAC, with a reliable automated system, will permit the real-time observation and quantification of parasitic structures, thanks also to artificial intelligence software, that is under development. For these reasons, the KFM is a promising tool for an accurate and efficient FEC to improve parasite diagnosis and to assist new generations of operators in veterinary and public health.

A smartphone microscopic method for simultaneous detection of (oo)cysts of Cryptosporidium and Giardia

Background

Food and water-borne illness caused by ingestion of (oo)cysts of Cryptosporidium and Giardia is one of the major health problems globally. Several methods are available to detect Giardia cyst and Cryptosporidium oocyst in food and water. Most of the available methods require a good laboratory facility and well-trained manpower and are therefore costly. There is a need of affordable and reliable method that can be easily implemented in resource limited settings.

Methodology/Principle findings

We developed a smartphone based microscopic assay method to screen (oo)cysts of Cryptosporidium and Giardia contamination of vegetable and water samples. The method consisting of a ball lens of 1 mm diameter, white LED as illumination source and Lugols's iodine staining provided magnification and contrast capable of distinguishing (oo)cysts of Cryptosporidium and Giardia. The analytical performance of the method was tested by spike recovery experiments. The spike recovery experiments performed on cabbage, carrot, cucumber, radish, tomatoes, and water resulted in 26.8±10.3, 40.1±8.5, 44.4±7.3, 47.6±11.3, 49.2 ±10.9, and 30.2±7.9% recovery for Cryptosporidium, respectively and 10.2±4.0, 14.1±7.3, 24.2±12.1, 23.2±13.7, 17.1±13.9, and 37.6±2.4% recovery for Giardia, respectively. The spike recovery results are comparable with data obtained using commercial brightfield and fluorescence microscope methods. Finally, we tested the smartphone microscope system for detecting (oo)cysts on 7 types of vegetable (n = 196) and river water (n = 18) samples. Forty-two percent vegetable and thirty-nine percent water samples were found to be contaminated with Cryptosporidium oocyst. Similarly, thirty-one percent vegetable and thirty-three percent water samples were contaminated with Giardia cyst.

Conclusions

The newly developed smartphone microscopic method showed comparable performance to commercial microscopic methods. The new method can be a low-cost and easy to implement alternative method for simultaneous detection of (oo)cysts in vegetable and water samples in resource limited settings.

Food and water-borne illness arising from the consumption of contaminated food and water are serious health hazards globally. Cryptosporidium and Giardia are the major food and water‒borne parasites. The infection occurs mainly by (oo)cyst phase of the parasites. People in developing countries are more vulnerable to these parasites where infection is more likely underdiagnosed and underreported due to limited resources for detection. There is need of a method that is affordable and easy to implement. In this study, we developed and optimized a novel smartphone microscope method that can detect and quantify the (oo)cyst of the parasites in food and water samples. The developed method is easy to implement and affordable and provides similar performance to the other commercially available microscopic methods.

Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples

Fecal egg counts are the cornerstone of equine parasite control programs. Previous work led to the development of an automated, image-analysis-based parasite egg counting system. The system has been further developed to include an automated reagent dispenser unit and a custom camera (CC) unit that generates higher resolution images, as well as a particle shape analysis (PSA) algorithm and machine learning (ML) algorithm. The first aim of this study was to conduct a comprehensive comparison of method precision between the original smartphone (SP) unit with the PSA algorithm, CC/PSA, CC/ML, and the traditional McMaster (MM) and Wisconsin (MW) manual techniques. Additionally, a Bayesian analysis was performed to estimate and compare sensitivity and specificity of all five methods. Feces were collected from horses, screened with triplicate Mini-FLOTAC counts, and placed into five categories: negative (no eggs seen), > 0 - ≤ 200 eggs per gram (EPG), > 200 - ≤ 500 EPG, > 500 - ≤ 1000 EPG, and > 1000 EPG. Ten replicates per horse were analyzed for each technique. Technical variability for samples > 200 EPG was significantly higher for MM than CC/PSA and CC/ML (p <  0.0001). Biological variability for samples> 0 was numerically highest for CC/PSA, but with samples > 200 EPG, MM had a significantly lower CV than MW (p =  0.001), MW had a significantly lower CV than CC/PSA (p <  0.0001), CC/ML had a significantly lower CV than both MW and SP/PSA (p <  0.0001, p =  0.0003), and CC/PSA had a significantly lower CV than CC/SP (p =  0.0115). Sensitivity was> 98 % for all five methods with no significant differences. Specificity, however, was significantly the highest for CC/PSA, followed numerically by SP/PSA, MM, CC/ML, and finally MW. Overall, the automated counting system is a promising new development in equine parasitology. Continued refinement to the counting algorithms will help improve precision and specificity, while additional research in areas such as egg loss, analyst variability at the counting step, and accuracy will help create a complete picture of its impact as a new fecal egg count method.

Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm

Background

Fecal examination is an important component of routine companion animal wellness exams. Sensitivity and specificity of fecal examinations, however, are influenced by sample preparation methodologies and the level of training and experience of personnel who read fecal slides. The VETSCAN IMAGYST system consists of three components: a sample preparation device, a commercially available scanner, and an analysis software. The VETSCAN IMAGYST automated scanner and cloud-based, deep learning algorithm, locates, classifies, and identifies parasite eggs found on fecal microscopic slides. The main study objectives were (i) to qualitatively evaluate the capabilities of the VETSCAN IMAGYST screening system and (ii) to assess and compare the performance of the VETSCAN IMAGYST fecal preparation methods to conventional fecal flotation techniques.

Methods

To assess the capabilities of VETSCAN IMAGYST screening components, fecal slides were prepared by the VETSCAN IMAGYST centrifugal and passive flotation techniques with 100 pre-screened fecal samples collected from dogs and cats and examined by both the algorithm and parasitologists. To determine the diagnostic sensitivity and specificity of the VETSCAN IMAGYST sample preparation techniques, fecal flotation slides were prepared by four different techniques (VETSCAN IMAGYST centrifugal and passive flotations, conventional centrifugal flotation, and passive flotation using OVASSAY® Plus) and examined by parasitologists. Additionally, required sample preparation and scanning times were estimated on a subset of samples to evaluate VETSCAN IMAGYST ease-of-use.

Results

The algorithm performance of the VETSCAN IMAGYST closely matched that of the parasitologists, with Pearsonʼs correlation coefficient (r) ranging from 0.83–0.99 across four taxa of parasites, Ancylostoma, Toxocara, Trichuris and Taeniidae. Both VETSCAN IMAGYST centrifugal and passive flotation methods correlated well with conventional preparation methods on all targeted parasites (diagnostic sensitivity of 75.8–100%, specificity of 91.8–100%, qualitative agreement between methods of 93.8–94.5%). Sample preparation, slide scan and image analysis were completed within 10–14 min by VETSCAN IMAGYST centrifugal and passive flotations, respectively.

Conclusions

The VETSCAN IMAGYST scanning system with the VETSCAN IMAGYST sample preparation methods demonstrated a qualitative match in comparison to the results of parasitologists’ examinations with conventional fecal flotation techniques. The VETSCAN IMAGYST is an easy-to-use, next generation qualitative and possibly quantitative diagnostic platform that brings expert clinical results into the hands of veterinary clinics.

Cattle gastrointestinal nematode egg-spiked faecal samples: high recovery rates using the Mini-FLOTAC technique

Background

Faecal egg count (FEC) techniques are commonly used to detect gastrointestinal nematodes (GINs) in cattle and to determine anthelmintic efficacy/resistance through the faecal egg count reduction test (FECRT). Mini-FLOTAC is one of the techniques recommended for a standardised FEC/FECRT of helminth eggs in cattle. However, only one paper evaluated the recovery rate of GIN eggs by Mini-FLOTAC (compared to McMaster and modified-Wisconsin method) in cattle, using only a level of contamination of 200 eggs per gram (EPG) of faeces and using GIN eggs collected from goat faeces to spike faecal samples from cattle. To further study the recovery rate of GIN eggs from cattle faeces, this study was conducted in two laboratories, one in Belgium and one in Italy to evaluate the sensitivity, accuracy, precision and reproducibility of the Mini-FLOTAC and McMaster techniques (at two reading levels: grids and chambers) for the detection of GIN eggs in spiked bovine faecal samples.

Methods

In both countries, spiked cattle faecal samples with five different levels of egg contamination (10, 50, 100, 200 and 500 EPG) of GINs were used. The study was performed in both laboratories by the same expert operator and using the same standard operating procedures (SOPs) for the Mini-FLOTAC and McMaster techniques. Sensitivity, accuracy and precision were calculated for each technique and for each level of contamination. Statistical analyses were performed to evaluate differences in performance between the two techniques.

Results

Mini-FLOTAC had a higher sensitivity (100% at all EPG levels for Mini-FLOTAC vs 0–66.6% for McMaster chambers and grids at levels< 100 EPG), a higher accuracy (98.1% mean value for Mini-FLOTAC vs 83.2% for McMaster grids and 63.8% for McMaster chambers) and a lower coefficient of variation (10.0% for Mini-FLOTAC vs 47.5% for McMaster grids and 69.4% for McMaster chambers) than McMaster. There was no significant difference in the recovery of GIN eggs between the two studies performed in Belgium and in Italy.

Conclusions

The high GIN egg recovery rate detected by Mini-FLOTAC and the similar results obtained in Belgium and in Italy indicated that the diagnostic performance of a FEC technique was not dependent on the laboratory environment.

Point-of-Care Diagnostics in Equine Practice

Point-of-care testing (POCT) refers to benchtop diagnostic modalities that have been translated into portable and easy-to-use formats suitable for patient-side use. Recent advances in diagnostic technology have allowed the development of a growing collection of POCT assays available to equine practitioners. Advantages include rapid results that reduce initial guesswork and promote diagnosis-targeted patient care, which may ultimately provide better clinical outcomes. Small handheld devices comprise most POCT technologies, providing qualitative or quantitative determination of an increasing range of analytes, including critical care analyzers and, more recently, hematology and immunology analyzers. This article discusses commercially available equine POCT.

The effect of counting duration on quantitative fecal egg count test performance

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Rapid counting reduces McMaster accuracy by 50–60% and precision by one third.

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Counting only one McMaster grid does not affect accuracy but decreases precision by one third.

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Automated counting operates with equal accuracy to McMaster but twice the precision.

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Strongylid ova suspended in sodium nitrate become translucent over time.

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Increased translucency is associated with a 5% underestimation of egg counts.

Fecal egg counts are the primary diagnostic tools of equine parasitology and use of the McMaster test and its variants in clinical practice is widely recommended. Manual counting is, however, prone to various sources of human error. For example, in real-world situations analysts can be under significant pressure to process high numbers of samples in a limited time. This practice could affect test result quality, but yet no studies have determined whether this is the case. This study’s purpose was to assess the effect of shortened counting duration (from either restricting counting time or counting only one grid of a slide) on McMaster test performance, and to compare the results to those of an automated test whose output is not subject to such limitations. Fifteen fecal samples from horses infected with strongylid parasites were divided equally into three groups based on high, medium and low levels of egg content (201–500, 501–1000 and 1001+ eggs/g). Slurries were produced from each sample and 10 subsamples of each were counted by both the McMaster and automated methods. McMaster slides were first counted at leisure, and then twice again with counting time being restricted to either one or two min. The effect of reducing sample processing time by counting only one grid of the McMaster slide was also assessed. Counting for one min significantly decreased manual egg counts by 50–60% relative to counts conducted at leisure (p < 0.001). While these decreases were somewhat ameliorated by counting for two min, the results were still approximately 10% lower than the at-leisure counts, a difference that was also statistically significant (p < 0.001). Furthermore, restricted counting duration also resulted in a significant decrease of approximately one-third in McMaster test precision, as assessed by the coefficients of variation (CoVs) of the 10 replicates of each sample, as did counting just a single grid of the McMaster slide. These differences effectively further improved the observed superior precision of the automated method compared to at-leisure manual McMaster counting, and the automated counts and their precision remained relatively unaffected following multiple analyses of the same processed samples. Taken together, these results indicate that analysists should carefully assess the possible effects on test performance of modifications to standard egg-counting procedures that are designed to account for real-world pressures, in order to achieve an optimal compromise between test accuracy and precision on one hand and practicality on the other.

Preventive chemotherapy and anthelmintic resistance of soil-transmitted helminths - Can we learn nothing from veterinary medicine?

Current parasite control programs in veterinary species have moved away from mass anthelmintic treatment approaches due to the emergence of significant anthelmintic resistance (AR), and the availability of few classes of anthelmintics. A number of parallels between livestock and human helminths exist that warn of the risk of AR in human soil-transmitted helminthiases, yet current public health interventions continue to prioritize mass treatment strategies, a known risk factor for AR. This review discusses the existing parallels between human and animal helminth biology and management, along with current public health recommendations and strategies for helminth control in humans. The effectiveness of current recommendations and alternative management strategies are considered.

Diagnosis of Human Trematode Infections

Digenetic trematodes form a major group of human parasites, affecting a large number of humans, especially in endemic foci. Over 100 species have been reported infecting humans, including blood, lung, liver, and intestinal parasites. Traditionally, trematode infections have been diagnosed by parasitological methods based on the detection and the identification of eggs in different clinical samples. However, this is complicated due to the morphological similarity between eggs of different trematode species and other factors such as lack of sensitivity or ectopic locations of the parasites. Moreover, the problem is currently aggravated by migratory flows, international travel, international trade of foods, and changes in alimentary habits. Although efforts have been made for the development of immunological and molecular techniques, the detection of eggs through parasitological techniques remains as the gold standard for the diagnosis of trematodiases. In this chapter, we review the current status of knowledge on diagnostic techniques used when examining feces, urine, and sputum and also analyze the most relevant characteristics used to identify eggs with a quick key for the identification of eggs.

A low-cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning

Manual hand counting of parasites in fecal samples requires costly components and substantial expertise, limiting its use in resource-constrained settings and encouraging overuse of prophylactic medication. To address this issue, a cost-effective, automated parasite diagnostic system that does not require special sample preparation or a trained user was developed. It is composed of an inexpensive (~US$350), portable, robotic microscope that can scan over the size of an entire McMaster chamber (100 mm² ) and capture high-resolution (~1 μm lateral resolution) bright field images without need for user intervention. Fecal samples prepared using the McMaster flotation method were imaged, with the imaging region comprising the entire McMaster chamber. These images are then automatically segmented and analyzed using a trained convolution neural network (CNN) to robustly separate eggs from background debris. Simple postprocessing of the CNN output yields both egg species and egg counts. The system was validated by comparing accuracy with hand-counts by a trained operator, with excellent performance. As a further demonstration of utility, the system was used to conveniently quantify drug response over time in a single animal, showing residual disease due to Anthelmintic resistance after 2 weeks.

Kankanet: An artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases

Background

Endemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis. This study pilots the use of smartphone microscopy and an artificial neural network-based (ANN) object detection application named Kankanet to address those two needs.

Methodology/Principal findings

A smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy.Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.9%) and specificity (97.1%) in A. lumbricoides to standard KK interpretation (97.0% sensitivity, 96.0% specificity). The UVC could not accurately image T. trichiura or hookworm. Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation, it still achieved high sensitivity for A. lumbricoides and hookworm (69.6% and 71.4%, respectively). Kankanet showed high sensitivity for T. trichiura in microscope images (100.0%), but low in UVC images (50.0%).

Conclusions/Significance

The UVC achieved comparable sensitivity to standard microscopy with only A. lumbricoides. With further improvement of image resolution and magnification, UVC shows promise as a point-of-care imaging tool. In addition to smartphone microscopy, ANN-based object detection can be developed as a diagnostic aid. Though trained with a limited dataset, Kankanet accurately interprets both standard microscope and low-quality UVC images. Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology.

For rainforest-enshrouded rural villages of Madagascar, soil-transmitted helminthiases are more the rule than the exception. However, the microscopy equipment and lab technicians needed for diagnosis are a distance of several days’ hike away. We piloted a solution for these communities by leveraging resources the villages already had: a traveling team of local health care workers, and their personal Android smartphones. We demonstrated that an inexpensive, commercially available microscope attachment for smartphones could rival the sensitivity and specificity of a regular microscope using standard field fecal sample processing techniques. We also developed an artificial neural network-based object detection Android application, called Kankanet, based on open-source programming libraries. Kankanet was used to detect eggs of the three most common soil-transmitted helminths: Ascaris lumbricoides, Trichuris trichiura, and hookworm. We found Kankanet to be moderately sensitive and highly specific for both standard microscope images and low-quality smartphone microscope images. This proof-of-concept study demonstrates the diagnostic capabilities of artificial neural network-based object detection systems. Since the programming frameworks used were all open-source and user-friendly even for computer science laymen, artificial neural network-based object detection shows strong potential for development of low-cost, high-impact diagnostic aids essential to health care and field research in resource-limited communities.

Rapid assessment of faecal egg count and faecal egg count reduction through composite sampling in cattle

Background

Faecal egg counts (FEC) and the FEC reduction test (FECRT) for assessing gastrointestinal nematode (GIN) infection and efficacy of anthelmintics are rarely carried out on ruminant farms because of the cost of individual analyses. The use of pooled faecal samples is a promising method to reduce time and costs, but few studies are available for cattle, especially on the evaluation of different pool sizes and FECRT application.

Methods

A study was conducted to assess FEC strategies based on pooled faecal samples using different pool sizes and to evaluate the pen-side use of a portable FEC-kit for the assessment of FEC on cattle farms. A total of 19 farms representing 29 groups of cattle were investigated in Italy and France. On each farm, individual faecal samples from heifers were collected before (D0) and two weeks after (D14) anthelmintic treatment with ivermectin or benzimidazoles. FEC were determined individually and as pooled samples using the Mini-FLOTAC technique. Four different pool sizes were used: 5 individual samples, 10 individual samples, global and global on-farm. Correlations and agreements between individual and pooled results were estimated with Spearman’s correlation coefficient and Lin’s concordance correlation coefficients, respectively.

Results

High correlation and agreement coefficients were found between the mean of individual FEC and the mean of FEC of the different pool sizes when considering all FEC obtained at D0 and D14. However, these parameters were lower for FECR calculation due to a poorer estimate of FEC at D14 from the faecal pools. When using FEC from pooled samples only at D0, higher correlation and agreement coefficients were found between FECR data, the better results being obtained with pools of 5 samples. Interestingly, FEC obtained on pooled samples by the portable FEC-kit on-farm showed high correlation and agreement with FEC obtained on individual samples in the laboratory. This field approach has to be validated on a larger scale to assess its feasibility and reliability.

Conclusions

The present study highlights that the pooling strategy and the use of portable FEC-kits on-farm are rapid and cost-effective procedures for the assessment of GIN egg excretion and can be used cautiously for FECR calculation following the administration of anthelmintics in cattle.

Reliability of three common fecal egg counting techniques for detecting strongylid and ascarid infections in horses

The detection and quantification of nematode eggs using fecal egg count techniques have an irreplaceable role in equine parasitic control. The reliability, particularly precision and accuracy, of individual techniques have been described only for strongylid infections. The aim of this study was to compare three fecal egg count techniques used for the detection of the two most common equine nematode infections: strongylid and ascarid. The Simple McMaster, Concentration McMaster and Mini-FLOTAC techniques were tested on spiked fecal samples with various levels of egg concentration (50, 100, 200, 500, 1000 and 3000 eggs per gram) and naturally infected mixed strongylid-ascarid samples with 30 replicates. The Simple McMaster, Concentration McMaster and Mini-FLOTAC techniques had precision coefficients of variation of 44.33, 35.64 and 18.25% for the strongylid infection and 62.95, 35.71 and 18.95% for the ascarid infection, and percent accuracies (mean count/number of eggs spiked) of 97.53, 88.39 and 74.18% for the strongylid infection and 65.53, 83.18 and 90.28% for the ascarid infection, respectively. Accuracy depended greatly on the type of nematode, but precision did not. The Mini-FLOTAC technique was more precise than the Simple and Concentration McMaster techniques regardless of nematode type. Simple McMaster was the most accurate technique for detecting strongylid eggs, and Mini-FLOTAC was the most accurate technique for detecting ascarid eggs. Our results indicated that none of the current techniques were universally and sufficiently reliable for the simultaneous quantification of both of these common equine nematodes.

The current status of anthelmintic resistance in a temperate region of Australia; implications for small ruminant farm management

Widespread anthelmintic resistance in small ruminants is a constraint on the profitability of the meat/wool industry. Limited published data is available on the prevalence and efficacy of anthelmintics, particularly in Australia where parasites affecting ruminant systems vary greatly between geographic regions. This paper reports on the anthelmintic resistance status in a temperate region of Victoria, Australia, a major sheep producing state largely affected by Trichostrongylus species and Teladorsagia circumcincta. The prevalence of anthelmintic resistance to any product was high (71%), with farms reporting varying levels of drug efficacies (21-100%). Resistance to older chemical groups (i.e. fenbendazole and levamisole) and single active macrocyclic lactone treatments was higher than newer chemical groups and combination treatments. This report provides clarity on anthelmintic resistance in the temperate region of Victoria and more importantly suggests that more comprehensive, regional specific anthelmintic resistance studies are required to understand the real level of chemical resistance threatening the effective control of worms.

Systematic review of gastrointestinal nematodes of horses from Australia

Background

Equine gastrointestinal nematodes (GINs) have been the subject of intermittent studies in Australia over the past few decades. However, comprehensive information on the epidemiology of equine GINs, the efficacy of available anthelmintic drugs and the prevalence of anthelmintic resistance (AR) in Australasia is lacking. Herein, we have systematically reviewed existing knowledge on the horse GINs recorded in Australia, and main aspects of their pathogeneses, epidemiology, diagnoses, treatment and control.

Methods

Six electronic databases were searched for publications on GINs of Australian horses that met our inclusion criteria for the systematic review. Subsets of publications were subjected to review epidemiology, diagnoses, pathogeneses, treatment and control of GINs of horses from Australia.

Results

A total of 51 articles published between 1950 to 2018 were included. The main GINs reported in Australian horses were cyathostomins (at least 28 species), Draschia megastoma, Habronema muscae, H. majus, Oxyuris equi, Parascaris equorum, Strongyloides westeri and Trichostrongylus axei across different climatic regions of Queensland, New South Wales, Victoria, and Western Australia. Nematodes are diagnosed based on the traditional McMaster egg counting technique, though molecular markers to characterise common GINs of equines were characterised in 1990s. The use of anthelmintic drugs remains the most widely-used strategy for controlling equine GIN parasites in Australia; however, the threshold of faecal egg count that should trigger treatment in horses, remains controversial. Furthermore, anthelmintic resistance within GIN population of horses is becoming a common problem in Australia.

Conclusions

Although GINs infecting Australian horses have been the subject of occasional studies over the past few decades, the effective control of GIN infections is hampered by a generalised lack of knowledge in various disciplines of equine parasitology. Therefore, coordinated and focused research is required to fill our knowledge gaps in these areas to maximise equine health and minimise economic losses associated with the parasitic infections in Australia.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3445-4) contains supplementary material, which is available to authorized users.

Mobile Phone Devices and Handheld Microscopes as Diagnostic Platforms for Malaria and Neglected Tropical Diseases (NTDs) in Low-Resource Settings: A Systematic Review, Historical Perspective and Future Outlook

The accurate, rapid, and cost-effective diagnosis of malaria and neglected tropical diseases (NTDs) in low-resource settings may benefit by significant technological advances in handheld and mobile phone microscopy. We systematically review the available literature in this field and discuss the future directions in which these technologies may be applied. English-language studies from the PubMed, Embase, and Web of Sciences were searched through April 2018 for observational and interventional studies reporting diagnostic characteristics of handheld and mobile phone microscopy devices as compared to field-established gold standard reference tests. Seventeen studies were included in the analysis. Findings included the high performance of the Newton Nm1 microscope in the diagnosis of Plasmodium species, Schistosoma mansoni, and soil-transmitted helminths (STHs), exhibiting sensitivity and specificity values often greater than 90%. Similarly, the CellScope was shown to have excellent diagnostic characteristics in the detection of Loa loa and Schistosoma species. Fluorescent microscopy was found to have high specificity and sensitivity in the diagnosis of Plasmodium species. Mobile phone technologies and handheld microscopes hold significant promise in the rapid and effective diagnosis of malaria and NTDs in areas where accurate diagnosis is vital. Although many of these technologies have yet to be securely embedded within the health system and studied directly in this context, the foundations for significant healthcare advances and impact have already been laid by several studies conducted within the last decade.

A modular, open-source, slide-scanning microscope for diagnostic applications in resource-constrained settings

In this paper we report the development of a cost-effective, modular, open source, and fully automated slide-scanning microscope, composed entirely of easily available off-the-shelf parts, and capable of bright field and fluorescence modes. The automated X-Y stage is composed of two low-cost micrometer stages coupled to stepper motors operated in open-loop mode. The microscope is composed of a low-cost CMOS sensor and low-cost board lenses placed in a 4f configuration. The system has approximately 1 micron resolution, limited by the f/# of available board lenses. The microscope is compact, measuring just 25×25×30 cm, and has an absolute positioning accuracy of ±1 μm in the X and Y directions. A Z-stage enables autofocusing and imaging over large fields of view even on non-planar samples, and custom software enables automatic determination of sample boundaries and image mosaicking. We demonstrate the utility of our device through imaging of fluorescent- and transmission-dye stained blood and fecal smears containing human and animal parasites, as well as several prepared tissue samples. These results demonstrate image quality comparable to high-end commercial microscopes at a cost of less than US$400 for a bright-field system, with an extra US$100 needed for the fluorescence module.

Control of helminth ruminant infections by 2030

Helminth infections have large negative impacts on production efficiency in ruminant farming systems worldwide, and their effective management is essential if livestock production is to increase to meet future human needs for dietary protein. The control of helminths relies heavily on routine use of chemotherapeutics, but this approach is unsustainable as resistance to anthelmintic drugs is widespread and increasing. At the same time, infection patterns are being altered by changes in climate, land-use and farming practices. Future farms will need to adopt more efficient, robust and sustainable control methods, integrating ongoing scientific advances. Here, we present a vision of helminth control in farmed ruminants by 2030, bringing to bear progress in: (1) diagnostic tools, (2) innovative control approaches based on vaccines and selective breeding, (3) anthelmintics, by sustainable use of existing products and potentially new compounds, and (4) rational integration of future control practices. In this review, we identify the technical advances that we believe will place new tools in the hands of animal health decision makers in 2030, to enhance their options for control and allow them to achieve a more integrated and sustainable approach to helminth control in support of animal welfare and production.

"Smart Diagnosis" of Parasitic Diseases by Use of Smartphones

Accurate and rapid diagnosis is crucial in combating parasitic diseases that cause millions of deaths worldwide. However, the scarcity of specialized diagnostic equipment in low- and middle-income countries is one of the barriers to effective management of parasitic diseases and warrants the need for alternative, inexpensive, point-of-care diagnostic tools. Due to their multiple built-in sensors, smartphones offer cost-effective alternative to expensive diagnostic devices. However, the use of smartphones in parasitic diagnoses remains in its infancy. This minireview describes various smartphone-based devices applied specifically for the diagnosis of parasitic diseases and discusses challenges and potential implications for their use in future.

Mind the gaps in research on the control of gastrointestinal nematodes of farmed ruminants and pigs

Gastrointestinal (GI) nematode control has an important role to play in increasing livestock production from a limited natural resource base and to improve animal health and welfare. In this synthetic review, we identify key research priorities for GI nematode control in farmed ruminants and pigs, to support the development of roadmaps and strategic research agendas by governments, industry and policymakers. These priorities were derived from the DISCONTOOLS gap analysis for nematodes and follow-up discussions within the recently formed Livestock Helminth Research Alliance (LiHRA). In the face of ongoing spread of anthelmintic resistance (AR), we are increasingly faced with a failure of existing control methods against GI nematodes. Effective vaccines against GI nematodes are generally not available, and anthelmintic treatment will therefore remain a cornerstone for their effective control. At the same time, consumers and producers are increasingly concerned with environmental issues associated with chemical parasite control. To address current challenges in GI nematode control, it is crucial to deepen our insights into diverse aspects of epidemiology, AR, host immune mechanisms and the socio-psychological aspects of nematode control. This will enhance the development, and subsequent uptake, of the new diagnostics, vaccines, pharma-/nutraceuticals, control methods and decision support tools required to respond to the spread of AR and the shifting epidemiology of GI nematodes in response to climatic, land-use and farm husbandry changes. More emphasis needs to be placed on the upfront evaluation of the economic value of these innovations as well as the socio-psychological aspects to prioritize research and facilitate uptake of innovations in practice. Finally, targeted regulatory guidance is needed to create an innovation-supportive environment for industries and to accelerate the access to market of new control tools.

Evaluation of accuracy and precision of a smartphone based automated parasite egg counting system in comparison to the McMaster and Mini-FLOTAC methods

Fecal egg counts are emphasized for guiding equine helminth parasite control regimens due to the rise of anthelmintic resistance. This, however, poses further challenges, since egg counting results are prone to issues such as operator dependency, method variability, equipment requirements, and time commitment. The use of image analysis software for performing fecal egg counts is promoted in recent studies to reduce the operator dependency associated with manual counts. In an attempt to remove operator dependency associated with current methods, we developed a diagnostic system that utilizes a smartphone and employs image analysis to generate automated egg counts. The aims of this study were (1) to determine precision of the first smartphone prototype, the modified McMaster and ImageJ; (2) to determine precision, accuracy, sensitivity, and specificity of the second smartphone prototype, the modified McMaster, and Mini-FLOTAC techniques. Repeated counts on fecal samples naturally infected with equine strongyle eggs were performed using each technique to evaluate precision. Triplicate counts on 36 egg count negative samples and 36 samples spiked with strongyle eggs at 5, 50, 500, and 1000 eggs per gram were performed using a second smartphone system prototype, Mini-FLOTAC, and McMaster to determine technique accuracy. Precision across the techniques was evaluated using the coefficient of variation. In regards to the first aim of the study, the McMaster technique performed with significantly less variance than the first smartphone prototype and ImageJ (p<0.0001). The smartphone and ImageJ performed with equal variance. In regards to the second aim of the study, the second smartphone system prototype had significantly better precision than the McMaster (p<0.0001) and Mini-FLOTAC (p<0.0001) methods, and the Mini-FLOTAC was significantly more precise than the McMaster (p=0.0228). Mean accuracies for the Mini-FLOTAC, McMaster, and smartphone system were 64.51%, 21.67%, and 32.53%, respectively. The Mini-FLOTAC was significantly more accurate than the McMaster (p<0.0001) and the smartphone system (p<0.0001), while the smartphone and McMaster counts did not have statistically different accuracies. Overall, the smartphone system compared favorably to manual methods with regards to precision, and reasonably with regards to accuracy. With further refinement, this system could become useful in veterinary practice.

Rationale for Quality Assurance in Fecal Egg Monitoring of Soil-Transmitted Helminthiasis

Substantial investment has been made into the once "neglected" tropical disease, soil-transmitted helminthiasis, and into control programs that operate within a framework of mapping baseline disease distribution, measuring the effectiveness of applied interventions, establishing when to cease drug administration, and for posttreatment evaluations. However, critical to each of these stages is the determination of helminth infection. The limitations of traditional microscope-based fecal egg diagnostics have not provided quality assurance in the monitoring of parasite disease and suboptimal treatment regimes provide for the potential development of parasite resistance to anthelmintic drugs. Improved diagnostic and surveillance tools are required to protect therapeutic effectiveness and to maintain funder confidence. Such tools may be on the horizon with emergent technologies that offer potential for enhanced visualization and quality-assured quantitation of helminth eggs.

Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health

Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings.