Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium.
Department of Theriogenology, University of Tehran, Tehran, Iran.
PLoS One. 2022 Jan 28;17(1):e0263409. doi: 10.1371/journal.pone.0263409. eCollection 2022.
The assessment of polymorphonuclear leukocyte (PMN) proportions (%) of endometrial samples is the hallmark for subclinical endometritis (SCE) diagnosis. Yet, a non-biased, automated diagnostic method for assessing PMN% in endometrial cytology slides has not been validated so far. We aimed to validate a computer vision software based on deep machine learning to quantify the PMN% in endometrial cytology slides. Uterine cytobrush samples were collected from 116 postpartum Holstein cows. After sampling, each cytobrush was rolled onto three different slides. One slide was stained using Diff-Quick, while a second was stained using Naphthol (golden standard to stain PMN). One single observer evaluated the slides twice at different days under light microscopy. The last slide was stained with a fluorescent dye, and the PMN% were assessed twice by using a fluorescence microscope connected to a smartphone. Fluorescent images were analyzed via the Oculyze Monitoring Uterine Health (MUH) system, which uses a deep learning-based algorithm to identify PMN. Substantial intra-method repeatabilities (via Spearman correlation) were found for Diff-Quick, Naphthol, and Oculyze MUH (r = 0.67 to 0.76). The intra-method agreements (via Kappa value) at ≥1% PMN (κ = 0.44 to 0.47) were lower than at >5 (κ = 0.69 to 0.78) or >10% (κ = 0.67 to 0.85) PMN cut-offs. The inter-method repeatabilities (via Lin's correlation) were also substantial, and values between Diff-Quick and Oculyze MUH, Naphthol and Diff-Quick, and Naphthol and Oculyze MUH were 0.68, 0.69, and 0.77, respectively. The agreements among evaluation methods at ≥1% PMN were weak (κ = 0.06 to 0.28), while it increased at >5 (κ = 0.48 to 0.81) or >10% (κ = 0.50 to 0.65) PMN cut-offs. To conclude, deep learning-based algorithms in endometrial cytology are reliable and useful for simplifying and reducing the diagnosis bias of SCE in dairy cows.
评估子宫内膜样本中多形核白细胞（PMN）的比例（%）是亚临床子宫内膜炎（SCE）诊断的标志。然而，到目前为止，还没有一种针对子宫内膜细胞学涂片上PMN%的无偏、自动化诊断方法得到验证。我们旨在验证一种基于深度学习的计算机视觉软件，以定量评估子宫内膜细胞学涂片上的PMN%。从 116 头产后荷斯坦奶牛中采集子宫细胞刷样本。采样后，每个细胞刷在三张不同的载玻片上滚动。一张载玻片用 Diff-Quick 染色，另一张载玻片用 Naphthol（染色PMN 的金标准）染色。一名观察者在不同的日子里在光学显微镜下两次评估载玻片。最后一张载玻片用荧光染料染色，两次通过连接到智能手机的荧光显微镜评估PMN%。通过 Oculyze Monitoring Uterine Health (MUH) 系统分析荧光图像，该系统使用基于深度学习的算法来识别PMN。Diff-Quick、Naphthol 和 Oculyze MUH 的方法内重复性（通过 Spearman 相关系数）很大（r = 0.67 至 0.76）。PMN 水平≥1%（κ=0.44 至 0.47）时的方法内一致性（通过 Kappa 值）低于PMN 水平>5%（κ=0.69 至 0.78）或>10%（κ=0.67 至 0.85）的一致性。方法间重复性（通过 Lin 相关系数）也很大，Diff-Quick 和 Oculyze MUH、Naphthol 和 Diff-Quick 以及 Naphthol 和 Oculyze MUH 之间的数值分别为 0.68、0.69 和 0.77。PMN 水平≥1%时，各评估方法之间的一致性较弱（κ=0.06 至 0.28），而PMN 水平>5%（κ=0.48 至 0.81）或>10%（κ=0.50 至 0.65）时，一致性增加。总之，基于深度学习的算法在子宫内膜细胞学中是可靠且有用的，可简化和减少奶牛 SCE 的诊断偏差。
