胚胎停育的影响因素分析及预测研究

doi:10.12280/gjszjk.20240148

摘要/Abstract

摘要：

胚胎停育（embryonic arrest）是早期妊娠的常见并发症，也是早期流产的主要原因之一。由于胚胎停育原因复杂多样，涉及遗传异常、免疫系统失调、母体健康状况和外部环境等多个因素，传统的诊断方法往往难以准确预测其发生。近年来，随着生物信息学和统计学的发展，医学研究人员开始运用随机森林模型、深度学习模型等人工智能技术构建胚胎停育的预测模型。这些模型可以帮助有效识别高风险群体并实施早期干预。然而胚胎停育的确切机制尚未完全阐明，预测模型的准确性和早期干预效果仍需进一步提升。未来的研究需加强遗传学和免疫学等领域的研究，改善预测模型的性能，为优化生殖健康的整体管理和提升人类的生活质量提供科学依据。

关键词: 胚胎发育, 影响因素分析, 人工智能, 神经网络, 计算机, 深度学习, 胚胎停育

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

李苗苗, 江洪, 蔡朋达. 胚胎停育的影响因素分析及预测研究[J]. 国际生殖健康/计划生育杂志, 2024, 43(4): 332-337.

LI Miao-miao, JIANG Hong, CAI Peng-da. Influence Factor Analysis and Forecasting Research of Embryonic Arrest[J]. Journal of International Reproductive Health/Family Planning, 2024, 43(4): 332-337.

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