Reproductive Outcomes After Intracytoplasmic Sperm Injection (ICSI) With Surgically Retrieved Sperm.

Overview

This research project explores how the source of sperm affects outcomes in assisted reproductive technologies (ART), focusing on children conceived through intracytoplasmic sperm injection (ICSI) with surgically retrieved sperm (SRS). Outcomes will be compared to those from ICSI with ejaculated sperm and conventional IVF. Using national registry data from all IVF and ICSI treatments in Sweden between 2007 and 2023 (Q-IVF), the study applies artificial intelligence (AI) and machine learning (ML) to identify factors influencing success rates. The aim is to improve prediction models and support more personalized fertility treatments. Special emphasis is placed on understanding the potential risks and long-term health outcomes for children conceived using SRS, which may be associated with higher rates of genetic abnormalities. The results may help optimize care strategies for infertile couples.

This research project aims to improve our understanding of how different sources of sperm affect outcomes in assisted reproductive technologies (ART). The focus is on children conceived through intracytoplasmic sperm injection (ICSI) using surgically retrieved sperm (SRS), compared to ICSI with ejaculated sperm and conventional in vitro fertilization (IVF). The study has three main goals: 1. To compare reproductive outcomes following ICSI with SRS, ICSI with ejaculated sperm, and conventional IVF. 2. To investigate whether artificial intelligence (AI) and machine learning (ML) algorithms can predict cumulative reproductive outcomes, including results from both fresh and frozen embryo transfer cycles. 3. To evaluate how AI/ML-based models perform in comparison to traditional statistical methods in terms of accuracy and predictive value. Method: National Population-Based Registry Study: This includes all IVF and ICSI treatments performed in Sweden from January 1, 2007, to December 31, 2023, using data from the Swedish National Quality Registry for Assisted Reproduction (Q-IVF). AI will be used to analyze large datasets, detect patterns, and help identify key factors-such as sperm source, number of retrieved oocytes, and patient characteristics-that may influence treatment success. This approach could lead to personalized treatment plans and better predictions of successful pregnancy outcomes. Special attention is given to outcomes in children born after ICSI with surgically retrieved sperm, as these sperm may be less mature and carry a higher risk of genetic abnormalities. The findings are expected to provide valuable insights into the reproductive and long-term health outcomes of these treatments, ultimately helping to improve care and treatment strategies for infertile couples.