Miyagi Yasunari, Habara Toshihiro, Hirata Rei, Hayashi Nobuyoshi
Medical Data Labo Okayama City Japan.
Department of Gynecologic Oncology Saitama Medical University International Medical Center Hidaka City Japan.
Reprod Med Biol. 2019 Feb 19;18(2):204-211. doi: 10.1002/rmb2.12267. eCollection 2019 Apr.
To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth.
A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI-based method with 5-fold cross-validation retrospectively for classifying embryos.
The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth ( < 0.005).
Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.
制作囊胚植入后期图像的人工智能（AI）分类器，以预测实现活产的概率。
开发了一种利用人工智能的机器学习方法（逻辑回归、朴素贝叶斯、最近邻、随机森林、神经网络和支持向量机）从囊胚图像预测活产概率的系统。使用80张导致活产的囊胚图像和80张导致非整倍体流产的囊胚图像，通过5折交叉验证回顾性地创建一种基于AI的方法来对胚胎进行分类。
逻辑回归方法显示出最佳结果。准确率、敏感性、特异性、阳性预测值和阴性预测值分别为0.65、0.60、0.70、0.67和0.64。曲线下面积为0.65±0.04（均值±标准误）。发现属于活产类别的估计概率与活产概率显著相关（<0.005）。
应用于囊胚图像的人工智能分类器有可能显示活产作为结果的概率。
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