Animal & Bioscience Department, Teagasc Mellows Campus, Athenry, Co., Galway, Ireland; Molecular Parasitology Laboratory, Centre for One Health and Ryan Institute, University of Galway, Galway, Ireland.
Animal & Bioscience Department, Teagasc Mellows Campus, Athenry, Co., Galway, Ireland.
Vet Parasitol. 2024 Jul;329:110216. doi: 10.1016/j.vetpar.2024.110216. Epub 2024 May 27.
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 (FECPAK, 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, FECPAK, 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 (FECPAK, 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 FECPAK 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 FECPAK; 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 FECPAK 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.
可持续的寄生虫控制措施对于减轻胃肠道线虫对动物健康和生产的负面影响以及减少抗寄生虫药物耐药性的选择压力是必要的。寄生虫诊断测试可以为治疗决策、治疗的时间和效果提供信息，并支持牲畜养殖计划。近年来，已经开发了一些新的诊断方法，其中一些方法结合了机器学习 (ML)，以促进寄生虫卵的检测和计数。在广泛实施之前，了解这些新方法与长期以来常用的方法相比的技术特点和性能非常重要。本研究的目的是试用三种新的基于图像分析的诊断工具（FECPAK、Micron 和 OvaCyte），并将其与传统的手动设备（McMaster 和 Mini-FLOTAC）进行比较。从 41 只自然感染胃肠道线虫的羔羊中获得粪便样本。将样本混合并分成 2 份，用于 5 种方法中的每一种方法的检查：McMaster、Mini-FLOTAC、FECPAK、Micron 和 OvaCyte。技术按照各自的标准方案进行，由经过培训的工作人员（McMaster 和 Mini-FLOTAC）或设备（FECPAK、Micron 和 OvaCyte）收集结果。关于马胃蝇虫卵数，McMaster 值从 0 到 9,000 个卵/克（EPG）不等。当比较重复的等分试样时，Mini-FLOTAC 和 Micron 方法与 McMaster 相比显示出相似的重复性。然而，我们发现 FECPAK 和 OvaCyte 比 McMaster 精度明显较低。当比较寄生虫卵计数时，在 McMaster 和所有其他方法之间建立了显著的正线性相关性。在 EPG 方面，McMaster 和 Mini-FLOTAC 或 FECPAK 之间没有差异；然而，与 McMaster 相比，Micron 和 OvaCyte 分别返回了显著更高和更低的 EPG。分配给其他寄生虫物种的卵数不足以在所有方法之间进行稳健的统计比较。然而，请注意，FECPAK 通常不会检测到食道口线虫卵，尽管其他方法可以检测到这些卵。此外，Moniezia spp 和 Trichuris spp 卵分别由 OvaCyte 和 Mini-FLOTAC 检测到，但其他方法没有检测到。传统方法和新的寄生虫诊断方法之间的观察到的差异突出表明需要继续培训和增强使用的机器学习模型，并需要为新开发的方法制定明确的验证指南。
