PDF(Pubmed)
Abstract：
UNASSIGNED: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV).
UNASSIGNED: In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script. The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes (PBC). The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control. Then, the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established.
UNASSIGNED: A total of 3769 vaginal secretion samples were collected. The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels. We selected 1.5 μm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates. The automated identification parameter of Lactobacillus was the ratio of bacteria ≥1.5 μm to those <1.5 μm. The cut-off scores were 2.5 and 0.5, In the parameter of white blood cells (WBC), the cut-off value of the absolute number of WBC was 103 μL－1 and the cut-off value of WBC-to-epithelial cell ratio was 10. The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1% and 15%. The parameter of background flora was bacteria<1.5 μm and the cut-off values were 5×103 μL－1 and 3×104 μL－1. The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1% and 10%. The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%. Out of 200 samples, automated microscopy and manual microscopy produced consistent scores for 185 samples, while the results for 15 samples were inconsistent.
UNASSIGNED: We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV. There was good overall concordance between automated microscopy and manual microscopy. The AI identification software for AV can complete clinical lab examination with rather high objectivity, sensitivity, and efficiency, markedly reducing the workload of manual microscopy.
UNASSIGNED: In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script. The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes (PBC). The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control. Then, the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established.
UNASSIGNED: A total of 3769 vaginal secretion samples were collected. The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels. We selected 1.5 μm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates. The automated identification parameter of Lactobacillus was the ratio of bacteria ≥1.5 μm to those <1.5 μm. The cut-off scores were 2.5 and 0.5, In the parameter of white blood cells (WBC), the cut-off value of the absolute number of WBC was 103 μL－1 and the cut-off value of WBC-to-epithelial cell ratio was 10. The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1% and 15%. The parameter of background flora was bacteria<1.5 μm and the cut-off values were 5×103 μL－1 and 3×104 μL－1. The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1% and 10%. The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%. Out of 200 samples, automated microscopy and manual microscopy produced consistent scores for 185 samples, while the results for 15 samples were inconsistent.
UNASSIGNED: We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV. There was good overall concordance between automated microscopy and manual microscopy. The AI identification software for AV can complete clinical lab examination with rather high objectivity, sensitivity, and efficiency, markedly reducing the workload of manual microscopy.
摘要：
■开发基于深度学习的人工智能阴道分泌物分析系统，并评估自动显微镜在需氧性阴道炎（AV）临床诊断中的准确性。
■在这项研究中，在妇产科接受治疗的3769名患者的阴道分泌物样本，华西第二医院,2020年1月至2021年12月的四川大学入选。使用手动显微镜的结果作为对照，我们开发了线性核SVM算法，人工智能（AI）自动分析软件，使用PythonScikit-learn脚本。AI自动分析软件可以识别具有毒性外观的白细胞和鼻旁上皮细胞（PBC）。使用乳酸菌和AV常见分离株的标准菌株重置细菌分级参数。采用受试者工作特征(ROC)曲线分析,以手动显微镜检测结果作为对照,确定不同评分项目的AV评价结果的截断值。然后，确定了自动房室识别的参数,初步建立了自动房室分析评分方法。
■收集总共3769个阴道分泌物样品。AI自动分析系统包含五个参数，每个参数包含三个严重程度评分级别。我们选择1.5μm作为乳杆菌和常见AV细菌分离株之间的直径的截断值。乳杆菌的自动化鉴定参数为≥1.5μm的细菌与<1.5μm的细菌的比率。白细胞（WBC）参数的截止分数分别为2.5和0.5，白细胞绝对数的截断值为103μL－1，白细胞与上皮细胞比值的截断值为10。毒性WBC的自动化鉴定参数为毒性WBC与WBC的比值,截止值分别为1%和15%。本底菌群参数为细菌<1.5μm，截止值为5×103μL－1和3×104μL－1。副基底上皮细胞的参数是PBC与上皮细胞的比率，临界值分别为1％和10％。自动显微镜的结果与手动显微镜的结果之间的一致率为92.5％。在200个样本中,自动显微镜和手动显微镜对185个样品产生一致的分数，而15个样本的结果不一致。
■我们开发了用于AV的AI识别软件，并建立了用于AV的自动阴道分泌物显微镜评分系统。自动显微镜和手动显微镜之间存在良好的整体一致性。AV的AI识别软件可以以相当高的客观性完成临床实验室检查，灵敏度,和效率，显着减少手动显微镜的工作量。
■在这项研究中，在妇产科接受治疗的3769名患者的阴道分泌物样本，华西第二医院,2020年1月至2021年12月的四川大学入选。使用手动显微镜的结果作为对照，我们开发了线性核SVM算法，人工智能（AI）自动分析软件，使用PythonScikit-learn脚本。AI自动分析软件可以识别具有毒性外观的白细胞和鼻旁上皮细胞（PBC）。使用乳酸菌和AV常见分离株的标准菌株重置细菌分级参数。采用受试者工作特征(ROC)曲线分析,以手动显微镜检测结果作为对照,确定不同评分项目的AV评价结果的截断值。然后，确定了自动房室识别的参数,初步建立了自动房室分析评分方法。
■收集总共3769个阴道分泌物样品。AI自动分析系统包含五个参数，每个参数包含三个严重程度评分级别。我们选择1.5μm作为乳杆菌和常见AV细菌分离株之间的直径的截断值。乳杆菌的自动化鉴定参数为≥1.5μm的细菌与<1.5μm的细菌的比率。白细胞（WBC）参数的截止分数分别为2.5和0.5，白细胞绝对数的截断值为103μL－1，白细胞与上皮细胞比值的截断值为10。毒性WBC的自动化鉴定参数为毒性WBC与WBC的比值,截止值分别为1%和15%。本底菌群参数为细菌<1.5μm，截止值为5×103μL－1和3×104μL－1。副基底上皮细胞的参数是PBC与上皮细胞的比率，临界值分别为1％和10％。自动显微镜的结果与手动显微镜的结果之间的一致率为92.5％。在200个样本中,自动显微镜和手动显微镜对185个样品产生一致的分数，而15个样本的结果不一致。
■我们开发了用于AV的AI识别软件，并建立了用于AV的自动阴道分泌物显微镜评分系统。自动显微镜和手动显微镜之间存在良好的整体一致性。AV的AI识别软件可以以相当高的客观性完成临床实验室检查，灵敏度,和效率，显着减少手动显微镜的工作量。
