Modelling follicular growth during ovarian stimulation using agent-based artificial intelligence
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Context
Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the ‘FSH window’ and induce multi-follicular growth. However, only limited data exist examining 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 (AI) 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 modelling 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 modelled to enable prediction of follicle sizes at the end of ovarian stimulation.
Results
Mean follicle growth rate of ovarian follicles was 1.35mm per day (95% CI 1.346-1.353), and was significantly associated with antral follicle count and FSH dose changes (both p < 0.001). Using only the first scan, the model enabled prediction of follicles sizes within 2mm 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 two 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 utilized advanced AI 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.
Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the ‘FSH window’ and induce multi-follicular growth. However, only limited data exist examining 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 (AI) 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 modelling 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 modelled to enable prediction of follicle sizes at the end of ovarian stimulation.
Results
Mean follicle growth rate of ovarian follicles was 1.35mm per day (95% CI 1.346-1.353), and was significantly associated with antral follicle count and FSH dose changes (both p < 0.001). Using only the first scan, the model enabled prediction of follicles sizes within 2mm 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 two 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 utilized advanced AI 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.
Date Issued
2026-03-01
Date Acceptance
2025-09-25
Citation
Journal of Clinical Endocrinology and Metabolism (JCEM), 2026, 111 (3), pp.615-621
ISSN
0021-972X
Publisher
Oxford University Press
Start Page
615
End Page
621
Journal / Book Title
Journal of Clinical Endocrinology and Metabolism (JCEM)
Volume
111
Issue
3
Copyright Statement
© The Author(s) 2025. Published by Oxford University Press on behalf of the Endocrine Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. See the journal About page for additional terms.
License URL
Publication Status
Published
Date Publish Online
2025-09-30