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Abstract：
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.
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.
摘要：
背景：数字成像与基于深度学习的计算图像分析相结合是医学诊断中不断增长的领域，包括寄生虫学,其中已经开发了许多自动分析设备，并可用于临床实践。
方法：ParasightAll-in-one（AIO）的性能，第二代设备，通过将其与公认的研究方法（mini-FLOTAC）和另一种市售测试（Imagyst）进行比较来进行评估。通过所有三种方法对59个犬和猫感染的粪便标本进行了定量分析。由于一些样本对一种以上的寄生虫呈阳性，该数据集包括48个Ancylostomaspp阳性标本。,13为弓形虫属。和23为毛虫属。
结果：ParasightAIO计数的大小与mini-FLOTAC的大小相关，但与Imagyst的大小无关。ParasightAIO计数的Ancylostomaspp卵增加了约3.5倍。和Trichurisspp.弓形虫属的卵子增加了4.6倍。而不是迷你FLOTAC,并计算了27.9-，这些相同的卵子比Imagyst多17.1倍和10.2倍，分别。这些差异转化为在低卵计数水平（<50卵/g）下测试灵敏度之间的差异，与ParasightAIO>mini-FLOTAC>Imagyst。在更高的卵数下，ParasightAIO和mini-FLOTAC以可比的精度进行（明显高于Imagyst），而在较低计数（>30个鸡蛋/g）时，Parasight比mini-FLOTAC和Imagyst更精确，而后两种方法没有显着差异。
结论：一般来说，ParasightAIO分析比mini-FLOTAC和Imagyst更精确，更灵敏，并且与mini-FLOTAC定量相关。虽然ParasightAIO生产的每克鸡蛋的原始计数低于mini-FLOTAC，这些可以使用从这些相关性产生的数据进行校正。
方法：ParasightAll-in-one（AIO）的性能，第二代设备，通过将其与公认的研究方法（mini-FLOTAC）和另一种市售测试（Imagyst）进行比较来进行评估。通过所有三种方法对59个犬和猫感染的粪便标本进行了定量分析。由于一些样本对一种以上的寄生虫呈阳性，该数据集包括48个Ancylostomaspp阳性标本。,13为弓形虫属。和23为毛虫属。
结果：ParasightAIO计数的大小与mini-FLOTAC的大小相关，但与Imagyst的大小无关。ParasightAIO计数的Ancylostomaspp卵增加了约3.5倍。和Trichurisspp.弓形虫属的卵子增加了4.6倍。而不是迷你FLOTAC,并计算了27.9-，这些相同的卵子比Imagyst多17.1倍和10.2倍，分别。这些差异转化为在低卵计数水平（<50卵/g）下测试灵敏度之间的差异，与ParasightAIO>mini-FLOTAC>Imagyst。在更高的卵数下，ParasightAIO和mini-FLOTAC以可比的精度进行（明显高于Imagyst），而在较低计数（>30个鸡蛋/g）时，Parasight比mini-FLOTAC和Imagyst更精确，而后两种方法没有显着差异。
结论：一般来说，ParasightAIO分析比mini-FLOTAC和Imagyst更精确，更灵敏，并且与mini-FLOTAC定量相关。虽然ParasightAIO生产的每克鸡蛋的原始计数低于mini-FLOTAC，这些可以使用从这些相关性产生的数据进行校正。
