Artificial Intelligence, Embryo Transfer, Reproductive Techniques, Assisted
Conditions
Keywords
Artificial intelligence, Embryo selection, IVF outcome, Time-lapse monitoring
Brief summary
In assisted reproductive technology (ART), selecting the most viable embryos from a large number of fertilized eggs is crucial. While techniques like morphological assessment, time-lapse monitoring systems, and pre-implantation genetic testing have improved the process, implantation success rates remain limited. The introduction of artificial intelligence in embryo evaluation, such as automated systems like EMA by AIVFTM, provides a promising alternative to enhance the accuracy and effectiveness of embryo selection. This study aims to assess the performance of the EMA system compared to traditional methods by examining its ability to rank embryos based on their potential for successful implantation, with the goal of increasing the chances of a successful pregnancy. This is the first clinical evaluation of this platform in France, offering new opportunities to improve decision-making in in-vitro fertilization.
Detailed description
This monocentric study analyzes 420 ART cycles from the Clermont-Ferrand University Hospital, conducted between January 2022 and December 2023. It includes IVF/ICSI cycles with embryos cultured in a Time-Lapse Incubator (Geri) and both fresh and frozen blastocyst transfers. A total of 1211 embryos were analyzed, with 619 used to train algorithms for predicting reproductive outcomes. This included 551 single and 34 double embryo transfer cycles, divided into 274 fresh and 345 frozen blastocyst transfers. For all blastocyst-stage embryos, the Geri score, Gardner classification, and EMA score based on time-lapse video were recorded for statistical analysis and evaluation of reproductive outcome predictions
Interventions
None listed
Sponsors
University Hospital, Clermont-Ferrand
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 60 Years
Healthy volunteers
No
Inclusion criteria
* IVF or ICSI treatment between January 2022 and December 2023 * Fresh or frozen sperm from the partner or a donor * Embryo culture up to the 105th hour * Fresh or frozen embryo transfer on Day 5 * Known implantation data (clinical pregnancy assessed by the presence of cardiac activity at 6 weeks via ultrasound)
Exclusion criteria
* Epididymal or testicular sperm * Embryo culture with transfer on Day 2 or Day 3 * ICSI with oocyte donation or the use of thawed oocytes * Transfer of two embryos resulting in a singleton pregnancy * No blastocyst obtained on Day 5
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of artificial intelligence (AI) Score and Laboratory Embryo Classification | 01/01/2022-31/12/2023 | The study aims to compare the EMA score assigned by the EMA platform (AiVFTM), an artificial intelligence-based tool, with the manual embryo classification performed by embryologists in the laboratory. This manual classification includes morphological assessment using the Gardner grading system and morphokinetic evaluation through the GERI score. The goal is to determine the level of concordance between the AI-generated scores and traditional embryologist assessments, and to evaluate the relationship and possible variability between the EMA score and the manual scoring methods. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To determine the association between EMA Score and implantation and live birth rates | 01/01/2022-31/12/2023 | To analyze the correlation between EMA scores and these key reproductive outcomes (implatation and live birth rate) |
| To evaluate the optimal EMA score threshold for maximizing live birth rates | 01/01/2022-31/12/2023 | To identify the optimal EMA score threshold that is most strongly associated with the highest live brith rates in assisted reproductive technology (ART). By analyzing the relationship between different EMA score levels and live birth outcomes, the study seeks to establish a predictive threshold that could guide embryo selection, thereby improving the likelihood of successful live birth following embryo transfer. |
Countries
France
Contacts
PRINCIPAL_INVESTIGATORFlorence Brugnon
University Hospital, Clermont-Ferrand
Outcome results
None listed