Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence
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 (ORCID: https://orcid.org/0000-0001-8693-8320)
- Sotirios H. Saravelos (ORCID: https://orcid.org/0000-0001-7079-6890)
- Scott McGill Nelson (ORCID: https://orcid.org/0000-0002-9099-3047)
- Geoffrey Trew (ORCID: https://orcid.org/0000-0001-8329-7513)
- Krasimira T. Tsaneva-Atanasova (ORCID: https://orcid.org/0000-0002-6294-7051)
- Thomas Heinis (ORCID: https://orcid.org/0000-0002-7470-2123)
- Alexander Comninos (ORCID: https://orcid.org/0000-0002-7104-2297)
- Waljit S. Dhillo (ORCID: https://orcid.org/0000-0001-5950-4316)
- Ali Abbara (ORCID: https://orcid.org/0000-0003-2795-5256)
- Tom Kelsey (ORCID: https://orcid.org/0000-0002-8091-1458)
- Anthony T. Yeung (ORCID: https://orcid.org/0000-0002-2029-4515)
- Margaritis Voliotis (ORCID: https://orcid.org/0000-0001-6488-7198)
- Simon Hanassab (ORCID: https://orcid.org/0000-0001-9112-0486)
- Rehan Salim
Institutions
- Imperial College Healthcare NHS Trust (GB)
- University of St Andrews (GB)
- University of Exeter (GB)
- Cancer Research UK Cambridge Center (GB)
- Oxford Fertility (GB)
- Imperial College London (GB)
- University of Glasgow (GB)
Publication Details
- Journal
- The Journal of Clinical Endocrinology & Metabolism
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1210/clinem/dgaf539
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Imperial Health Charity
- UK Research and Innovation
- National Institute for Health and Care Research
- Wellbeing of Women
- Imperial College London
- Medical Research Council