Quantitative approaches in clinical reproductive endocrinology. (December 2022)

• Record Type: Journal Article
• Authors: Voliotis, Margaritis; Hanassab, Simon; Abbara, Ali; Heinis, Thomas; Dhillo, Waljit S.; Tsaneva-Atanasova, Krasimira
• Is Part Of: Current opinion in endocrine and metabolic research. Volume 27(2022)
• Publication Date: 2022-12
• Subjects: Assisted reproductive technology -- Machine learning -- Mathematical modelling -- Pulsatility analysis -- Clinical decision making -- In vitro fertilization -- Artificial intelligence -- Reproductive endocrinology -- Quantitative modelling; HPG hypothalamic-pituitary gonadal -- ART assisted reproductive technology -- GnRH gonadotropin-releasing hormone -- LH luteinizing hormone -- FSH follicle-stimulating hormone -- PCOS polycystic ovary syndrome -- HA hypothalamic amenorrhea -- BSA Bayesian Spectrum Analysis -- AI artificial intelligence -- ML machine learning -- IVF in vitro fertilization -- AMH anti-Müllerian hormone -- OHSS ovarian hyperstimulation syndrome -- E2 estradiol -- P4 progesterone; Endocrine glands -- Diseases -- Periodicals; Metabolism -- Disorders -- Periodicals; Endocrine System Diseases; Metabolic Diseases; Endocrine glands -- Diseases; Metabolism -- Disorders; Periodical; Electronic journals; Periodicals; Electronic journals; 616.4
• DOI: 10.1016/j.coemr.2022.100421
• Languages: English
• ISSNs: 2451-9650

Abstract

Abstract: 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.