Application of artificial neural network in evaluation of IVF embryo / 中华检验医学杂志

ABSTRACT

Artificial neural network (ANN) is a network framework that drives artificial intelligence (AI). Classical convolutional neural networks (CNN) are mainly used for cell count and image recognition at fixed time in embryo evaluation. Fully connected deep neural networks (DNN), with increased accuracy of image recognition, are suitable for the units equipped with high configuration hardware and need comprehensive prediction according to the integrated clinical information. Residual networks improve the accuracy by increasing layers and solving the gradient disappearance problem through jump connection to realize dynamic embryo assessment. Bayesian networks (BN) and multi-layer perceptron (MLP) are two machine learning methods. The former is especially used for comprehensive prediction combined with complex clinical information in case of lack of conditions. The latter has gradient disappearance and explosion problem, and is easy to lose some spatial features of images, so it is used for small sample volumes. ANN has advantages in the prediction of implantation rate and aneuploidy and reducing invasive detection in quality assessment of embryos, which is an important research direction of human-assisted reproductive technology (ART).