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Quantitative approaches in clinical reproductive endocrinology.

In Current opinion in endocrine and metabolic research

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.

Voliotis Margaritis, Hanassab Simon, Abbara Ali, Heinis Thomas, Dhillo Waljit S, Tsaneva-Atanasova Krasimira

2022-Dec

AI, artificial intelligence, AMH, anti-Müllerian hormone, ART, assisted reproductive technology, Artificial intelligence, Assisted reproductive technology, BSA, Bayesian Spectrum Analysis, Clinical decision making, E2, estradiol, FSH, follicle-stimulating hormone, GnRH, gonadotropin-releasing hormone, HA, hypothalamic amenorrhea, HPG, hypothalamic-pituitary gonadal, IVF, in vitro fertilization, In vitro fertilization, LH, luteinizing hormone, ML, machine learning, Machine learning, Mathematical modelling, OHSS, ovarian hyperstimulation syndrome, P4, progesterone, PCOS, polycystic ovary syndrome, Pulsatility analysis, Quantitative modelling, Reproductive endocrinology