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Quantitative approaches in clinical reproductive endocrinology

Margaritis Voliotis1, Simon Hanassab2,3,4, Ali Abbara2

  • 1Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.

Current Opinion in Endocrine and Metabolic Research
|January 16, 2023
PubMed

Keywords:
AI, artificial intelligenceAMH, anti-Müllerian hormoneART, assisted reproductive technologyArtificial intelligenceAssisted reproductive technologyBSA, Bayesian Spectrum AnalysisClinical decision makingE2, estradiolFSH, follicle-stimulating hormoneGnRH, gonadotropin-releasing hormoneHA, hypothalamic amenorrheaHPG, hypothalamic-pituitary gonadalIVF, in vitro fertilizationIn vitro fertilizationLH, luteinizing hormoneML, machine learningMachine learningMathematical modellingOHSS, ovarian hyperstimulation syndromeP4, progesteronePCOS, polycystic ovary syndromePulsatility analysisQuantitative modellingReproductive endocrinology

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