EFRE 40: An AI-generated predictive algorithm for IVF success in AMA
Maghraby, H., Gaafar, S., Abo Ali, A., Elmahdy, M., Adel, N., Hassan, H., Fawzy, R. (2025). EFRE 40: An AI-generated predictive algorithm for IVF success in AMA. EKB Journal Management System, 1(4), 276-285. doi: 10.21608/jrme.2025.369219.1035
Hassan Maghraby; Sherif Gaafar; Ashraf Abo Ali; Mohamed Elmahdy; Nehal Adel; Heba Hassan; Rana Fawzy. "EFRE 40: An AI-generated predictive algorithm for IVF success in AMA". EKB Journal Management System, 1, 4, 2025, 276-285. doi: 10.21608/jrme.2025.369219.1035
Maghraby, H., Gaafar, S., Abo Ali, A., Elmahdy, M., Adel, N., Hassan, H., Fawzy, R. (2025). 'EFRE 40: An AI-generated predictive algorithm for IVF success in AMA', EKB Journal Management System, 1(4), pp. 276-285. doi: 10.21608/jrme.2025.369219.1035
Maghraby, H., Gaafar, S., Abo Ali, A., Elmahdy, M., Adel, N., Hassan, H., Fawzy, R. EFRE 40: An AI-generated predictive algorithm for IVF success in AMA. EKB Journal Management System, 2025; 1(4): 276-285. doi: 10.21608/jrme.2025.369219.1035

EFRE 40: An AI-generated predictive algorithm for IVF success in AMA

Journal of Reproductive Medicine and Embryology
Article 10, Volume 1, Issue 4, February 2025, Page 276-285  XML PDF (446.81 K)
Document Type: Debate
DOI: 10.21608/jrme.2025.369219.1035
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Authors
Hassan Maghraby email orcid 1, 2; Sherif Gaafar1, 2, 3; Ashraf Abo Ali2, 3; Mohamed Elmahdyorcid 1, 2; Nehal Adelorcid 2, 3; Heba Hassan2, 4; Rana Fawzy5
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.
5Electrical engineering department, faculty of engineering Alexandria, Egypt
Abstract
Natural Language Processing (NLP), a subset of artificial intelligence (AI), enables computers to interpret and generate human language. Machine learning (ML) models are increasingly employed to analyze clinical, hormonal, and embryological data to predict in vitro fertilization (IVF) success rates.the success rates of IVF, particularly among women over the age of 40, remain a significant concern. Advancements in artificial intelligence (AI) offer promising avenues to enhance predictive modeling in this domain, potentially improving clinical outcomes This study aims to develop a predictive model using Meta AI to estimate live birth probabilities in women over 40 undergoing IVF. By inputting specific variables into the AI model, we seek to create a tool that can assist clinicians and patients in making informed decisions about fertility treatments, ultimately improving personalized care in this demographic. The model was created using a theoretical framework, without real-world patient data. The model included; embryo quality, uterine receptivity, maternal age, sperm quality and previuos pregnancy outcome. The aim of the model is to predict clinical pregnency rate and live birth rate.
Keywords
Prediction models; Advanced maternal age; Artificial intelligence
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