Published online Sep 28, 2020. doi: 10.35711/aimi.v1.i3.94
Peer-review started: August 24, 2020
First decision: September 13, 2020
Revised: September 19, 2020
Accepted: September 19, 2020
Article in press: September 19, 2020
Published online: September 28, 2020
Processing time: 34 Days and 15.3 Hours
To acquire live births is the goal of assisted reproductive technology. No method has been established in practice to use non-morphological analysis and/or morphological analysis such as conventional morphological evaluations and time-lapse microscopy to predict the live birth of a blastocyst.
Artificial intelligence (AI) classifiers for blastocyst images to predict the live birth has been introduced in reproductive medicine recently.
The present study aimed to develop an AI classifier that combines blastocyst images and the morphological features and clinical information of the conventional embryo evaluation parameters such as maternal age to predict the probability of achieving a live birth.
A total of 5691 images of blastocysts combined with conventional embryo evaluation parameters were used. A system in which the original architecture of the deep learning neural network was developed to predict the probability of live birth.
The number of independent clinical information for predicting live birth is 10. The best single AI classifier composed of ten layers of convolutional neural networks and each elementwise layer of ten factors was 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 were 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively.
AI classifiers have the potential of predicting live births that humans cannot predict. AI that can be trained by both morphological and non- morphological information may make progress in assisted reproductive technology.
Due to the development of AI that does not harm the embryo, the embryo can be transferred after making the prediction. AI could bring benefits to the advancement of assisted reproductive technology.