Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence.

Journal: The Journal of clinical endocrinology and metabolism
Published Date: Feb 20, 2026

Abstract

CONTEXT: Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the "FSH window" and induce multifollicular growth. However, only limited data exist that examine individual follicular growth rates during fertility treatment. OBJECTIVE: To model growth rates of individual ovarian follicles during ovarian stimulation in MAR cycles using an agent-based artificial intelligence model. DESIGN: Observational cohort study. SETTING: Eleven assisted conception clinics in Europe. PATIENTS: 11 572 patients (2005-2023) who underwent ovarian stimulation during MAR. INTERVENTION: Predictive modeling was conducted using 39 698 scans including 434 082 follicles from 12 950 cycles during ovarian stimulation. MAIN OUTCOME MEASURES: Daily growth rates of individual ovarian follicles during stimulation were modeled to enable prediction of follicle sizes at the end of ovarian stimulation. RESULTS: Mean follicle growth rate of ovarian follicles was 1.350 mm/day (95% CI: 1.346-1.353 mm/day) and was significantly associated with antral follicle count and FSH dose changes (both P < .001). Using only the first scan, the model enabled prediction of follicles sizes within 2 mm at the end of ovarian stimulation with 75.0% accuracy (95% CI: 74.6-75.3%), increasing to 80.1% (95% CI: 79.8-80.5%) when incorporating the first 2 scans. Predictive performance was stable across clinics, with a mean accuracy of 78.0% in a random training-test split, and 77.1% using cross-validation by clinic. CONCLUSION: We used advanced artificial intelligence techniques to progress our understanding of follicle growth dynamics during ovarian stimulation. This model can reliably predict follicle size profiles at the end of stimulation enabling moderation of the number of scans required.

Authors

  • Artsiom Hramyka
    School of Computer Science, University of St Andrews, St Andrews, UK.
  • Thomas W Kelsey
    University of St Andrews, School of Computer Science, St Andrews KY16 9SX, United Kingdom.
  • Simon Hanassab
    Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
  • Scott M Nelson
    School of Medicine, University of Glasgow, Glasgow G31 2ER, UK.
  • Arthur C Yeung
    Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
  • Sotirios Saravelos
    Imperial College Healthcare NHS Trust, London W2 1NY, United Kingdom.
  • Rehan Salim
    Imperial College Healthcare NHS Trust, London, UK.
  • Alexander N Comninos
    Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
  • Krasimira Tsaneva-Atanasova
    Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
  • Margaritis Voliotis
    University of Exeter, Department of Mathematics and Statistics, Exeter EX4 4QF, United Kingdom.
  • Geoffrey H Trew
    Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
  • Thomas Heinis
    Department of Computing, Imperial College London, London, UK.
  • Waljit S Dhillo
    Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
  • Ali Abbara
    Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK. [email protected].

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