Study Design and Population in IVF Cycles: Insights from Angers Hospital

The investigation into the complexities of In Vitro Fertilization (IVF) has become increasingly relevant, given its profound implications for reproductive health. At the Reproductive Medicine Center in Angers Hospital, France, a retrospective observational study was undertaken to glean insights from IVF cycles performed from January 2020 to February 2023. This article delves into the intricate study design and the carefully selected population that formed the basis of this investigation.

Research Context and Aim

This study emerged from a critical need to understand the factors influencing IVF success. By assessing women aged between 18 to 43 years—an age range consistent with French standard requirements for IVF—the study aimed to establish a comprehensive correlational analysis of clinical outcomes based on embryo culture data. Women who underwent stimulation cycles leading to multiple embryo transfers (both fresh and frozen) were included in the research.

Defining Key Outcomes

Two primary outcomes were of interest: clinical pregnancy (denoted as positive known implantation data, KIDp) and implantation failure (negative known implantation data, KIDn). This dichotomy allowed for a focused analysis that elucidated the factors contributing to successful implantation versus those leading to failure.

Population Selection: Inclusion and Exclusion Criteria

The process of selecting participants for this research study was meticulous, adhering to stringent inclusion and exclusion criteria to ensure the reliability of the findings.

Inclusion Criteria

• Women aged between 18 and 43 years
• Embryos cultured in the EmbryoScope+® time-lapse incubator
• Stimulation cycles resulting in multiple transfers (both fresh and frozen)

Exclusion Criteria

• Cases with inadequate embryo quality or developmental anomalies
• Patients with prior medical histories that could contribute confounding variables

These criteria aimed to create a homogenous study population with comparable characteristics, which is essential for deriving meaningful conclusions.

Ovarian Stimulation and Laboratory Procedures

Understanding the complexity of the ovarian stimulation process is vital, as it establishes the groundwork for embryo cultivation and subsequent assessments.

Hormonal Protocols

In this study, pituitary suppression was achieved either through:

1. Antagonists (e.g., Ganirelix at 0.25 mg daily)
2. Gonadotropin-Releasing Hormone Agonists (e.g., Triptorelin at 0.1 mg subcutaneously daily)

Ovarian stimulation followed, utilizing recombinant or urine-derived Follicle Stimulating Hormone (FSH). The selection of gonadotropin dosage was tailored based on individual patient characteristics, including markers of ovarian reserve and previous responses to stimulation.

Oocyte Retrieval and Insemination Insights

Triggering ovulation for oocyte retrieval was contingent on specific criteria: a minimum of three follicles measuring ≥ 17 mm and certain serum estradiol levels. After performing ultrasound-guided transvaginal oocyte retrieval, the collected oocytes and cumulus complexes were meticulously prepared for insemination via in vitro fertilization (IVF) or Intracytoplasmic Sperm Injection (ICSI)—decisions dictated by sperm quality and past fertilization histories.

Embryo Culture and Assessment

The nuances of embryo culture significantly affect IVF outcomes. Post-insemination, embryos were cultured in the EmbryoScope+® time-lapse incubator, allowing for continuous monitoring and assessment. This platform provided invaluable data regarding embryonic development, enabling the categorization of embryos based on various morphological and morphokinetic parameters.

Assessment Criteria

1. Morphological Assessment: Embryos were graded on parameters like cell number, fragmentation level, and development symmetry.
2. Blastocyst Quality: Good-quality blastocysts were defined by specific criteria, including expansion grade and quality of the inner cell mass.
3. Morphokinetic Monitoring: Key embryonic developmental milestones were tracked using automated tools and human assessments.

Data Collection and Processing Techniques

Data collection involved exporting raw videos from the EmbryoViewer software. However, preprocessing these videos was essential due to the large file sizes and potential visual artifacts. Deep learning techniques, specifically a YOLO v6 object detection algorithm, were harnessed to accurately identify and locate embryos in the videos.

Image Preprocessing and Analysis

The preprocessing steps included:

• Cropping images around the embryo, eliminating irrelevant frames.
• Training the YOLO algorithm on a curated dataset to enable precise embryo detection.
• Manual review facilitated high-quality output, ensuring that only relevant images were analyzed for further insights.

Advanced Feature Extraction Through Deep Learning

The innovative aspect of this study lay in its application of advanced deep learning techniques to extract and analyze embryonic features.

Pre-Training and Fine Tuning

Using a self-supervised contrastive learning approach, the researchers trained a deep learning model to identify and map static morphological features of the embryos. This was further complemented by a supervised one-shot learning framework aimed at predicting pregnancy outcomes based on dynamic morphokinetic features.

Validation Methodologies

The model validation encompassed two integral tasks:

1. Predicting clinical pregnancy outcomes based on previous outcomes from a given cohort.
2. Determining outcomes without relying on historical data.

Both methodologies utilized sophisticated machine learning models to ensure robust predictions and insights.

Statistical Analysis and Computational Tools

The researchers employed rigorous statistical analyses to evaluate the performance of their predictive models. Area Under the Curve (AUC) metrics, confidence intervals, and performance measures such as F1-score, sensitivity, and specificity were meticulously calculated to ascertain the efficacy of the model against a random chance baseline.

Utilization of Computational Resources

Python and various libraries were leveraged for data processing, analysis, and model training, showcasing the synergy between technology and reproductive medicine.

Through this exhaustive study design and thorough methodology, the Angers Hospital research team presents a pivotal exploration into IVF outcomes, integrating advanced technology with clinical practice to pave the way for more effective fertility treatments in the future.