Abstract:

Embryonic arrest, a common complication in early pregnancy, is one of the primary causes of early miscarriage. The causes of embryonic arrest are complex and multifaceted, involving genetic abnormalities, immune system dysregulation, maternal health conditions, and external environmental factors. Therefore, traditional diagnostic methods are difficult to accurately predict the occurrence of embryonic arrest. In recent years, with the advances in bioinformatics and statistics, artificial intelligence technologies, such as random forests model and deep learning model, have been used to construct the predictive models of embryonic arrest. These predictive models can effectively identify the high-risk groups and the implement of early interventions. However, the precise mechanisms of embryonic arrest are not yet fully understood, and the accuracy of predictive models and the effectiveness of early interventions still need further enhancement. Future research should be focused on strengthening studies in genetics and immunology to improve the performance of predictive models, thereby providing a scientific basis for optimizing overall reproductive health management and enhancing the quality of human life.

Key words: Embryonic development, Root cause analysis, Artificial intelligence, Neural networks, computer, Deep learning, Embryonic arrest