ABSTRACT
Sustainable parasite control practices are necessary to combat the negative effects of gastrointestinal nematodes on animal health and production while reducing the selection pressure for anthelmintic resistance. Parasite diagnostic tests can inform treatment decisions, the timing and effectiveness of treatment and enable livestock breeding programmes. In recent years new diagnostic methods have been developed, some incorporating machine learning (ML), to facilitate the detection and enumeration of parasite eggs. It is important to understand the technical characteristics and performance of such new methods compared to long standing and commonly utilised methods before they are widely implemented. The aim of the present study was to trial three new diagnostic tools relying on image analysis (FECPAKG2, Micron and OvaCyte) and to compare them to traditional manual devices (McMaster and Mini-FLOTAC). Faecal samples were obtained from 41 lambs naturally infected with gastrointestinal nematodes. Samples were mixed and separated into 2 aliquots for examination by each of the 5 methods: McMaster, Mini-FLOTAC, FECPAKG2, Micron and OvaCyte. The techniques were performed according to their respective standard protocols and results were collected by trained staff (McMaster and Mini-FLOTAC) or by the device (FECPAKG2, Micron and OvaCyte). Regarding strongyle worm egg count, McMaster values varied from 0 to 9,000 eggs per gram (EPG). When comparing replicate aliquots, both the Mini-FLOTAC and Micron methods displayed similar repeatability to McMaster. However, we found FECPAKG2 and OvaCyte significantly less precise than McMaster. When comparing parasite egg enumeration, significant positive linear correlations were established between McMaster and all other methods. No difference was observed in EPG between McMaster and Mini-FLOTAC or FECPAKG2; however, Micron and OvaCyte returned significantly higher and lower EPG, respectively, compared to McMaster. The number of eggs ascribed to other parasite species was not sufficient for performing a robust statistical comparison between all methods. However, it was noted that FECPAKG2 generally did not detect Strongyloides papillosus eggs, despite these being detected by other methods. In addition, Moniezia spp and Trichuris spp eggs were detected by OvaCyte and Mini-FLOTAC, respectively, but not by other methods. The observed variation between traditional and new methods for parasite diagnostics highlights the need for continued training and enhancing of ML models used and the importance of developing clear guidelines for validation of newly developed methods.
