AI-Driven Prediction of Oocyte Retrieval in Older Women with Low Ovarian Reserve, a model based on systematic literature review

Maghraby, Hassan; Elmahdy, Mohamed; Adel, Nehal; Aboali, Ashraf; Saleh, Hesham; Amer, Medhat; Zaki, Hossam; Maghraby, Heba

doi:10.21608/jrme.2025.384368.1049

AI-Driven Prediction of Oocyte Retrieval in Older Women with Low Ovarian Reserve, a model based on systematic literature review

Document Type : Original Article

Authors

¹ Obstetrics and Gynecology Department, Faculty of Medicine, Alexandria University, Egypt.

² Egyptian Foundation of Reproductive Medicine and Embryology (EFRE), Egypt.

³ Madina Fertility Center Madina Women Hospital, Alexandria, Egypt.

⁴ Agial Fertility Center, Agial Hospital, Alexandria, Egypt.

⁵ Andrology Department, Faculty of Medicine, Cairo University, Egypt.

⁶ Adam International Hospital, Cairo, Egypt.

⁷ Ganin Fertility Center, Cairo, Egypt.

⁸ Adam and Hawa IVF center. Alexandria, Egypt.

10.21608/jrme.2025.384368.1049

Abstract

Objective: To introduce a potential paradigm shift in ovarian stimulation for women aged 40 and above, focusing on the rationale for mild stimulation doses, supported by a novel predictive AI model.
Methods: The "EFRE Predict AI model," also termed the "AI Accelerated Knowledge Synthesis Paradigm," was developed using an AI-driven methodology. This involved an autonomous systematic literature review (SLR) guided by the PICO framework to synthesize predictive insights from high-quality scientific literature, including international guidelines and primary research. The model is designed to calculate the number of obtainable oocytes based on patient-specific factors: Age, Anti-Müllerian Hormone (AMH), Antral Follicle Count (AFC), Body Mass Index (BMI), previous stimulation history, and gonadotropin doses used (75, 150, 225, and 300 IU). Initial validation included a survey of over 100 experts and specialists.
Results: The development process yielded the EFRE Predict AI model capable of estimating oocyte yield based on key clinical parameters. Initial validation through expert surveys indicated very satisfactory acceptance among specialists.
Conclusion: The EFRE Predict AI model offers a novel tool to personalize ovarian stimulation protocols for women over 40. By providing data-driven predictions of oocyte yield at varying gonadotropin doses, including mild doses, this AI-accelerated knowledge synthesis approach supports a potential paradigm shift towards more individualized and potentially milder stimulation strategies in this patient population.

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