Quantitative approaches in clinical reproductive endocrinology

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

doi:10.1016/j.coemr.2022.100421

Quantitative approaches in clinical reproductive endocrinology

Curr Opin Endocr Metab Res. 2022 Dec:27:100421. doi: 10.1016/j.coemr.2022.100421.

Authors

Margaritis Voliotis¹, Simon Hanassab^{2

3

4}, Ali Abbara², Thomas Heinis³, Waljit S Dhillo², Krasimira Tsaneva-Atanasova¹

Affiliations

¹ Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
² Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
³ Department of Computing, Imperial College London, London, United Kingdom.
⁴ UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom.

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.

Keywords: 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.

Publication types

Review