In Human fertility (Cambridge, England)
With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.
Yuan Guanghui, Lv Bohan, Hao Cuifang
2023-Jan-11
** embryo quality assessment, Artificial intelligence, artificial neural networks, assisted reproduction, personalised diagnosis and treatment, prediction model**
