EFRE IVF 40; AI model for personalized PGTA decision in women Over 40
Maghraby, H., Elmahdy, M., Gaafar, S., Aboali, A., Adel, N., ElKassar, Y., Hassan, H. (2025). EFRE IVF 40; AI model for personalized PGTA decision in women Over 40. EKB Journal Management System, 1(4), 286-297. doi: 10.21608/jrme.2025.370139.1037
Hassan Maghraby; Mohamed Elmahdy; Sherif Gaafar; Ashraf Aboali; Nehal Adel; Yasser ElKassar; Heba Hassan. "EFRE IVF 40; AI model for personalized PGTA decision in women Over 40". EKB Journal Management System, 1, 4, 2025, 286-297. doi: 10.21608/jrme.2025.370139.1037
Maghraby, H., Elmahdy, M., Gaafar, S., Aboali, A., Adel, N., ElKassar, Y., Hassan, H. (2025). 'EFRE IVF 40; AI model for personalized PGTA decision in women Over 40', EKB Journal Management System, 1(4), pp. 286-297. doi: 10.21608/jrme.2025.370139.1037
Maghraby, H., Elmahdy, M., Gaafar, S., Aboali, A., Adel, N., ElKassar, Y., Hassan, H. EFRE IVF 40; AI model for personalized PGTA decision in women Over 40. EKB Journal Management System, 2025; 1(4): 286-297. doi: 10.21608/jrme.2025.370139.1037

EFRE IVF 40; AI model for personalized PGTA decision in women Over 40

Journal of Reproductive Medicine and Embryology
Article 11, Volume 1, Issue 4, February 2025, Pages 286-297    PDF (478.39 K)
Document Type: Original Article
DOI: 10.21608/jrme.2025.370139.1037
Authors
Hassan Maghraby* 1, 2; Mohamed Elmahdy1, 2; Sherif Gaafar1, 2, 3; Ashraf Aboali2, 3; Nehal Adel2, 3; Yasser ElKassar1, 2; Heba Hassan2, 4
1Obstetrics and Gynecology Department, Faculty of Medicine, Alexandria University, Egypt.
2Egyptian Foundation of Reproductive Medicine and Embryology (EFRE), Egypt.
3Madina Fertility Center, Madina Women's Hospital, Alexandria, Egypt.
4Dar Alteb Infertility Center, Alexandria, Egypt.
Abstract
Background:
Female fertility declines sharply after 40 due to diminished ovarian reserve, compromised oocyte quality, and a significant increase in aneuploidy rates. In vitro fertilization (IVF) strategies, including repeated cycles, embryo pooling, and preimplantation genetic testing for aneuploidy (PGT-A), present complex decision-making challenges requiring personalized, data-driven approaches.
Objective:
This study aims to develop and evaluate AI-driven predictive models using Meta AI to optimize fertility treatment strategies in women over 40, assisting in selecting between repeated IVF cycles and embryo pooling with PGT-A.
Methods:
A generative AI model (Llama 3.2) was employed to extract and synthesize data from peer-reviewed literature. Expert systems were integrated to formulate a structured decision-support framework, leveraging a knowledge base and inference engine. Two predictive models were developed:
1. Pre-Treatment Evaluation Score (PTES) – Assesses overall fertility potential.
2. Embryo Quality Refinement Score (EQRS) – Incorporates embryo-specific factors to refine treatment recommendations.
Results:
Both models demonstrated high predictive accuracy and clinical relevance. Higher scores indicated a preference for embryo pooling with PGT-A, while lower scores supported repeated IVF cycles. Performance metrics, including precision and AUC-ROC, confirmed the models' efficacy in predicting live birth probability.
Conclusion:
AI-driven predictive modeling offers a novel, data-supported approach to personalized fertility care. These models facilitate evidence-based decision-making, potentially improving clinical outcomes. Further validation in real-world clinical settings is warranted.
Keywords
AI-driven predictive modeling; advanced maternal age; embryo pooling; embryo accumulation; PGTA
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