Abstract
Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or female reproductive system, presents a global challenge due to its impact on one in six individuals worldwide. Given the high prevalence of infertility and the limited available resources in fertility care, infertility creates substantial obstacles to reproductive autonomy and places a considerable burden on fertility care providers. While existing research are exploring to use artificial intelligence (AI) methods to assist fertility care providers in managing in vitro fertilization (IVF) cycles, these attempts fail in accurately predicting specific aspects such as medication dosage and intermediate ovarian responses during controlled ovarian stimulation (COS) within IVF cycles. Our current work developed Edwards, a deep learning model based on the Transformer-Encoder architecture to improve the prediction outcomes. Edwards is designed to capture the temporal features within the sequential process of IVF cycles, It could provide the options of treatment plans as well as predict hormone profiles, and ovarian responses at any stage upon both current and historical data. By considering the full context of the process, Edwards demonstrates improved accuracy in predicting the final outcomes of the IVF process compared to previous approaches based on traditional machine learning. The strength of our current deep learning model stems from its ability to learn the intricate endocrinological mechanisms of the female reproductive system, especially for the context of COS in IVF cycles.