BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst ...BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst is evaluated by microscopic inspection,and the result is only about 30%-40%,and the method of predicting live birth from the blastocyst image is unknown.Live births correlate with several clinical conventional embryo evaluation parameters(CEE),such as maternal age.Therefore,it is necessary to develop artificial intelligence(AI)that combines blastocyst images and CEE to predict live births.AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option.We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.RESULTS The live birth rate was 0.387(=1587/4104 cases).The number of independent clinical information for predicting live birth is 10,which significantly avoids multicollinearity.A single AI classifier is composed of ten layers of convolutional neural networks,and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value.The accuracy,sensitivity,specificity,negative predictive value,positive predictive value,Youden J index,and area under the curve values for predicting live birth are 0.743,0.638,0.789,0.831,0.573,0.427,and 0.740,respectively.The optimal cut-off point of the receiver operator characteristic curve is 0.207.CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict.Artificial intelligence may make progress in assisted reproductive technology.展开更多
文摘BACKGROUND The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine.When the selected blastocyst is transferred to the uterus,the degree of implantation of the blastocyst is evaluated by microscopic inspection,and the result is only about 30%-40%,and the method of predicting live birth from the blastocyst image is unknown.Live births correlate with several clinical conventional embryo evaluation parameters(CEE),such as maternal age.Therefore,it is necessary to develop artificial intelligence(AI)that combines blastocyst images and CEE to predict live births.AIM To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.METHODS A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option.We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.RESULTS The live birth rate was 0.387(=1587/4104 cases).The number of independent clinical information for predicting live birth is 10,which significantly avoids multicollinearity.A single AI classifier is composed of ten layers of convolutional neural networks,and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value.The accuracy,sensitivity,specificity,negative predictive value,positive predictive value,Youden J index,and area under the curve values for predicting live birth are 0.743,0.638,0.789,0.831,0.573,0.427,and 0.740,respectively.The optimal cut-off point of the receiver operator characteristic curve is 0.207.CONCLUSION AI classifiers have the potential of predicting live births that humans cannot predict.Artificial intelligence may make progress in assisted reproductive technology.
