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

The groundbreaking impact of digitalization and artificial intelligence in sheep farming

The integration of digitalization and Artificial Intelligence (AI) has marked the onset of a new era of efficient sheep farming in multiple aspects ranging from the general well-being of sheep to advanced web-based management applications. The resultant improvement in sheep health and consequently better farming yield has already started to benefit both farmers and veterinarians. The predictive analytical models embedded with machine learning (giving sense to machines) has helped better decision-making and has enabled farmers to derive most out of their farms. This is evident in the ability of farmers to remotely monitor livestock health by wearable devices that keep track of animal vital signs and behaviour. Additionally, veterinarians now employ advanced AI-based diagnostics for efficient parasite detection and control. Overall, digitalization and AI have completely transformed traditional farming practices in livestock animals. However, there is a pressing need to optimize digital sheep farming, allowing sheep farmers to appreciate and adopt these innovative systems. To fill this gap, this review aims to provide available digital and AI-based systems designed to aid precision farming of sheep, offering an up-to-date understanding on the subject. Various contemporary techniques, such as sky shepherding, virtual fencing, advanced parasite detection, automated counting and behaviour tracking, anomaly detection, precision nutrition, breeding support, and several mobile-based management applications are currently being utilized in sheep farms and appear to be promising. Although artificial intelligence and machine learning may represent key features in the sustainable development of sheep farming, they present numerous challenges in application.

Computer vision model for the detection of canine pododermatitis and neoplasia of the paw

BACKGROUND

Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology.

ANIMALS

Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used.

OBJECTIVES

We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia.

MATERIALS AND METHODS

The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed.

RESULTS

A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS.

CONCLUSIONS AND CLINICAL RELEVANCE

This novel object detection model has the potential for application in the field of veterinary dermatology.

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%.

Toward automating the diagnosis of gastrointestinal parasites in cats and dogs

Diagnosing gastrointestinal parasites by microscopy slide examination often leads to human interpretation errors, which may occur due to fatigue, lack of training and infrastructure, presence of artifacts (e.g., various types of cells, algae, yeasts), and other reasons. We have investigated the stages in automating the process to cope with the interpretation errors. This work presents advances in two stages focused on gastrointestinal parasites of cats and dogs: a new parasitological processing technique, named TF-Test VetPet, and a microscopy image analysis pipeline based on deep learning methods. TF-Test VetPet improves image quality by reducing cluttering (i.e., eliminating artifacts), which favors automated image analysis. The proposed pipeline can identify three species of parasites in cats and five in dogs, distinguishing them from fecal impurities with an average accuracy of 98,6%. We also make available the two datasets with images of parasites of dogs and cats, which were obtained by processing fecal smears with temporary staining using TF-Test VetPet.

World Association for the Advancement of Veterinary Parasitology (W.A.A.V.P.) guideline for diagnosing anthelmintic resistance using the faecal egg count reduction test in ruminants, horses and swine

The faecal egg count reduction test (FECRT) remains the method of choice for establishing the efficacy of anthelmintic compounds in the field, including the diagnosis of anthelmintic resistance. We present a guideline for improving the standardization and performance of the FECRT that has four sections. In the first section, we address the major issues relevant to experimental design, choice of faecal egg count (FEC) method, statistical analysis, and interpretation of the FECRT results. In the second section, we make a series of general recommendations that are applicable across all animals addressed in this guideline. In the third section, we provide separate guidance details for cattle, small ruminants (sheep and goats), horses and pigs to address the issues that are specific to the different animal types. Finally, we provide overviews of the specific details required to conduct an FECRT for each of the different host species. To address the issues of statistical power vs. practicality, we also provide two separate options for each animal species; (i) a version designed to detect small changes in efficacy that is intended for use in scientific studies, and (ii) a less resource-intensive version intended for routine use by veterinarians and livestock owners to detect larger changes in efficacy. Compared to the previous FECRT recommendations, four important differences are noted. First, it is now generally recommended to perform the FECRT based on pre- and post-treatment FEC of the same animals (paired study design), rather than on post-treatment FEC of both treated and untreated (control) animals (unpaired study design). Second, instead of requiring a minimum mean FEC (expressed in eggs per gram (EPG)) of the group to be tested, the new requirement is for a minimum total number of eggs to be counted under the microscope (cumulative number of eggs counted before the application of a conversion factor). Third, we provide flexibility in the required size of the treatment group by presenting three separate options that depend on the (expected) number of eggs counted. Finally, these guidelines address all major livestock species, and the thresholds for defining reduced efficacy are adapted and aligned to host species, anthelmintic drug and parasite species. In conclusion, these new guidelines provide improved methodology and standardization of the FECRT for all major livestock species.

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.

Deep learning-based diagnosis of feline hypertrophic cardiomyopathy

Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the gold standards in the diagnosis of feline HCM. However, only 75% accuracy has been achieved using radiography alone. Therefore, we trained five residual architectures (ResNet50V2, ResNet152, InceptionResNetV2, MobileNetV2, and Xception) using 231 ventrodorsal radiographic images of cats (143 HCM and 88 normal) and investigated the optimal architecture for diagnosing feline HCM through radiography. To ensure the generalizability of the data, the x-ray images were obtained from 5 independent institutions. In addition, 42 images were used in the test. The test data were divided into two; 22 radiographic images were used in prediction analysis and 20 radiographic images of cats were used in the evaluation of the peeking phenomenon and the voting strategy. As a result, all models showed > 90% accuracy; Resnet50V2: 95.45%; Resnet152: 95.45; InceptionResNetV2: 95.45%; MobileNetV2: 95.45% and Xception: 95.45. In addition, two voting strategies were applied to the five CNN models; softmax and majority voting. As a result, the softmax voting strategy achieved 95% accuracy in combined test data. Our findings demonstrate that an automated deep-learning system using a residual architecture can assist veterinary radiologists in screening HCM.

Implementing deep learning models for the classification of Echinococcus multilocularis infection in human liver tissue

BACKGROUND

The histological diagnosis of alveolar echinococcosis can be challenging. Decision support models based on deep learning (DL) are increasingly used to aid pathologists, but data on the histology of tissue-invasive parasitic infections are missing. The aim of this study was to implement DL methods to classify Echinococcus multilocularis liver lesions and normal liver tissue and assess which regions and structures play the most important role in classification decisions.

METHODS

We extracted 15,756 echinococcus tiles from 28 patients using 59 whole slide images (WSI); 11,602 tiles of normal liver parenchyma from 18 patients using 33 WSI served as a control group. Different pretrained model architectures were used with a 60-20-20% random splitting. We visualized the predictions using probability-thresholded heat maps of WSI. The area-under-the-curve (AUC) value and other performance metrics were calculated. The GradCAM method was used to calculate and visualize important spatial features.

RESULTS

The models achieved a high validation and test set accuracy. The calculated AUC values were 1.0 in all models. Pericystic fibrosis and necrotic areas, as well as germinative and laminated layers of the metacestodes played an important role in decision tasks according to the superimposed GradCAM heatmaps.

CONCLUSION

Deep learning models achieved a high predictive performance in classifying E. multilocularis liver lesions. A possible next step could be to validate the model using other datasets and test it against other pathologic entities as well, such as, for example, Echinococcus granulosus infection.

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.

Worms and bugs of the gut: the search for diagnostic signatures using barcoding, and metagenomics-metabolomics

Gastrointestinal (GI) helminth infections cause significant morbidity in both humans and animals worldwide. Specific and sensitive diagnosis is central to the surveillance of such infections and to determine the effectiveness of treatment strategies used to control them. In this article, we: (i) assess the strengths and limitations of existing methods applied to the diagnosis of GI helminth infections of humans and livestock; (ii) examine high-throughput sequencing approaches, such as targeted molecular barcoding and shotgun sequencing, as tools to define the taxonomic composition of helminth infections; and (iii) discuss the current understanding of the interactions between helminths and microbiota in the host gut. Stool-based diagnostics are likely to serve as an important tool well into the future; improved diagnostics of helminths and their environment in the gut may assist the identification of biomarkers with the potential to define the health/disease status of individuals and populations, and to identify existing or emerging anthelmintic resistance.

Canine Vaginal Cytology: A Revised Definition of Exfoliated Vaginal Cells

Vaginal cytology is an important examination method in the context of gynecological disorders and cycle staging in the bitch. While collection and preparation of samples are easy, the evaluation appears to be challenging. Inconsistent definitions of cell attributes such as size, cornification and the appearance of the nucleus have been published. The aim of the project was to develop a tutorial for vaginal cell determination. To get a deeper insight into the use of cytology in practice, an online survey was distributed to veterinarians interested in small animal reproduction. Participants were asked to define eight cells and answer questions. The agreement of the 16 participants, working in eight different countries, determining the cells was poor (κ = 0.412). Eleven respondents stated that vaginal cytology has a low reliability. Nevertheless, 13 participants use this tool regularly. The tutorial was developed as a flowchart based on the survey results, scientific literature and own measurements. It guides the user systematically through the evaluation of specific cell characteristics. An evaluation of the results of five raters with difference experience levels led to a high agreement (κ = 0.858). Vaginal cytology is a useful diagnostic tool, but it seems helpful to standardize the determination of cell types.

Anthelmintic resistance in ruminants: challenges and solutions

Anthelmintic resistance (AR) is a growing concern for effective parasite control in farmed ruminants globally. Combatting AR will require intensified and integrated research efforts in the development of innovative diagnostic tests to detect helminth infections and AR, sustainable anthelmintic treatment strategies and the development of complementary control approaches such as vaccination and plant-based control. It will also require a better understanding of socio-economic drivers of anthelmintic treatment decisions, in order to support a behavioural shift and develop targeted communication strategies that promote the uptake of evidence-based sustainable solutions. Here, we review the state-of-the-art in these different fields of research activity related to AR in helminths of livestock ruminants in Europe and beyond. We conclude that in the advent of new challenges and solutions emerging from continuing spread of AR and intensified research efforts, respectively, there is a strong need for transnational multi-actor initiatives. These should involve all key stakeholders to develop indicators of infection and sustainable control, set targets and promote good practices to achieve them.

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.

Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals

The increasingly close proximity between people and animals is of great concern for public health, given the risk of exposure to infectious diseases transmitted through animals, which are carriers of more than 60 zoonotic agents. These diseases, which are included in the list of Neglected Tropical Diseases, cause losses in countries with tropical and subtropical climates, and in regions with temperate climates. Indeed, they affect more than a billion people around the world, a large proportion of which are infected by one or more parasitic helminths, causing annual losses of billions of dollars. Several studies are being conducted in search for differentiated, more sensitive diagnostics with fewer errors. These studies, which involve the automated examination of intestinal parasites, still face challenges that must be overcome in order to ensure the proper identification of parasites. This includes a protocol that allows for elimination of most of the debris in samples, satisfactory staining of parasite structures, and a robust image database. Our objective here is therefore to offer a critical description of the techniques currently in use for the automated diagnosis of intestinal parasites in fecal samples, as well as advances in these techniques.

What makes a good fecal egg count technique?

The first parasite fecal egg counting techniques were described over 100 years ago, and fecal egg counting remains essential in parasitology research as well as in clinical practice today. Several novel techniques have been introduced and validated in recent years, but this work has also highlighted several current issues in this research field. There is a lack of consensus on which diagnostic parameters to evaluate and how to properly design studies doing so. Furthermore, there is a confusing and sometimes incorrect use of terminology describing performance of fecal egg counting techniques, and it would be helpful to address these. This manuscript reviews qualitative and quantitative diagnostic performance parameters, discusses their relevance for fecal egg counting techniques, and highlights some of the challenges with determining them. Qualitative parameters such as diagnostic sensitivity and specificity may be considered classic diagnostic performance metrics, but they generally only have implications at low egg count levels. The detection limit of a given technique is often referred to as the "analytical sensitivity", but this is misleading as the detection limit is a theoretically derived number, whereas analytical sensitivity is determined experimentally. Thus, the detection limit is not a diagnostic performance parameter and does not inform on the diagnostic sensitivity of a technique. Quantitative performance parameters such as accuracy and precision are highly relevant for describing the performance of fecal egg counting techniques, and precision is arguably the more important of the two. An absolute determination of accuracy can only be achieved by use of samples spiked with known quantities of parasite ova, but spiking does not necessarily mimic the true distribution of eggs within a sample, and accuracy estimates are difficult to reproduce between laboratories. Instead, analysis of samples from naturally infected animals can be used to achieve a relative ranking of techniques according to egg count magnitude. Precision can be estimated in a number of different approaches, but it is important to ensure a relevant representation of egg count levels in the study sample set, as low egg counts tend to associate with lower precision estimates. Coefficients of variation generally provide meaningful measures of precision that are independent of the multiplication factor of the techniques evaluated. Taken together, there is a need for clear guidelines for studies validating fecal egg counting techniques in veterinary parasitology with emphasis on what should be evaluated, how studies could be designed, and how to appropriately analyze the data. Furthermore, there is a clear need for better consensus regarding use of terminology describing the diagnostic performance of fecal egg count techniques.

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.

How qPCR complements the WHO roadmap (2021-2030) for soil-transmitted helminths

Complementing the launch of the World Health Organization (WHO) roadmap (2021-2030) we explore key elements needing attention before recruitment of qPCR as the main diagnostics tool to confirm reduction or elimination of soil-transmitted helminth (STH) transmission in both control and elimination programmes. Given the performance limitations of conventional methods, a proposed harmonised qPCR will provide a diagnostic tool, with the sensitivity and specificity required to monitor low-intensity infections, following mass drug administration (MDA). Technical and logistical challenges associated with introducing qPCR as a stand-alone tool are highlighted, and a decision-making scheme on how qPCR can support surveillance, resistance detection, and elimination is presented. An accurate point-of-care (POC) diagnostic test needs to be developed to support STH control in the field, and STH biorepositories need to be established and maintained to ensure that reference materials are available for research and validation.

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.