PDF(Pubmed)
Abstract：
OBJECTIVE: To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy.
METHODS: A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom.
RESULTS: When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos).
CONCLUSIONS: By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.
METHODS: A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method. Patient characteristics such as maternal age, AMH level, paternal sperm quality, and total number of normally fertilized (2PN) embryos were processed using SVM and NN. To improve model performance, we created voting ensembles using CNN, SVM, and NN to combine our imaging data with clinical parameter variations. Statistical significance was evaluated with a one-sample t-test with 2 degrees of freedom.
RESULTS: When assessing blastocyst images alone, the CNN test accuracy was 61.2% (± 1.32% SEM, n = 3 models) in correctly classifying euploid/aneuploid embryos (n = 140 embryos). When the best CNN model was assessed as a voting ensemble, the test accuracy improved to 65.0% (AMH; p = 0.1), 66.4% (maternal age; p = 0.06), 65.7% (maternal age, AMH; p = 0.08), 66.4% (maternal age, AMH, number of 2PNs; p = 0.06), and 71.4% (maternal age, AMH, number of 2PNs, sperm quality; p = 0.02) (n = 140 embryos).
CONCLUSIONS: By combining CNNs with patient characteristics, voting ensembles can be created to improve the accuracy of classifying embryos as euploid/aneuploid from CNN alone, allowing for AI to serve as a potential non-invasive method to aid in karyotype screening and selection of embryos.
摘要：
目标：要确定是否创建结合卷积神经网络（CNN）的投票集合，支持向量机(SVM)，和多层神经网络（NN）与临床参数一起提高了人工智能（AI）作为预测非整倍体的非侵入性方法的准确性。
方法：使用699天5PGT-A测试的胚泡进行训练，验证,并测试CNN将胚胎分类为整倍体/非整倍体。使用改良的FAST-SeqS下一代测序方法分析所有胚胎。患者特征，如产妇年龄，AMH水平,父系精子质量，使用SVM和NN处理正常受精(2PN)胚胎的总数。为了提高模型性能，我们用CNN创建了投票合奏，SVM,和NN将我们的影像学数据与临床参数变化相结合。用具有2个自由度的单样本t检验评价统计学显著性。
结果：单独评估胚泡图像时，CNN测试准确率为61.2%(±1.32%SEM，n=3个模型）正确分类整倍体/非整倍体胚胎（n=140个胚胎）。当最好的CNN模型被评估为投票合奏时，测试精度提高到65.0%(AMH；p=0.1)，66.4%(产妇年龄；p=0.06)，65.7%(孕产妇年龄，AMH;p=0.08),66.4%(产妇年龄,AMH,2PN的数量；p=0.06)，和71.4%(产妇年龄，AMH,2PN的数量，精子质量；p=0.02）（n=140个胚胎）。
结论：通过将CNN与患者特征相结合，可以创建投票集合来提高仅从CNN将胚胎分类为整倍体/非整倍体的准确性，允许AI作为一种潜在的非侵入性方法来帮助核型筛选和胚胎选择。
方法：使用699天5PGT-A测试的胚泡进行训练，
结果：单独评估胚泡图像时，
结论：通过将CNN与患者特征相结合，
