P-122 Evaluation of an updated artificial intelligence embryo viability model on implantation and miscarriage
Authors: Gabrielsen A V , Johansen M N , Iversen L H , Montag M , Berntsen J 
Published: June 2023

Abstract

Study question
Is the implantation and miscarriage prediction performance of the iDAScore model v2.0 increased as compared to the original v1.0 model?

Summary answer
The predictive performance is better for iDAScore v2.0 than for v1.0 on both implantation and miscarriage, although the differences did not reach statistical significance.

What is known already
In recent years, several artificial intelligence (AI) models for the prediction of embryo implantation based on image or time-lapse data have been published and validated in various ways. Some of these models are commercial products while others are more of an academic nature. The long-term benefit of AI models in clinical practice relies on their use in clinical routine and that the models are continuously monitored and updated. The iDAScore time-lapse model has been available from Vitrolife since 2021 and several external validations of iDAScore v1.0 have been published. An updated version (iDAScore v2.0) has recently been released.

Study design, size, duration
This was an observational retrospective study with data collected during the years 2019 to 2022 from one Danish clinic from fresh single embryo transfers performed on day 5. Blastocysts were selected for transfer by experienced embryologists based on morphology (Gardner score). Within the study period, we included all 849 embryos for which we had information on hCG test results, implantation (fetal heartbeat (FH) detected by ultrasound) and live birth (LB).

Participants/materials, setting, methods
Embryos were cultured in an EmbryoScope+ incubator (Vitrolife A/S). Time-lapse images were automatically captured throughout the incubation period. We used the original time-lapse images to calculate scores for iDAScore v1.0 and v2.0 retrospectively. For each version, the predictive performance for miscarriage (early pregnancy loss and clinical miscarriage), FH and LB prediction was expressed in terms of area under the ROC curve (AUC). Differences in performance between versions were assessed in paired comparisons of the AUCs.

Main results and the role of chance
The median age of the transfer recipients was 34 years. One third of the blastocysts was derived from ICSI and the remaining from standard IVF cycles. Among all transferred blastocysts, the hCG rate was 58.0% (492/849), the FH rate was 46.5% (395/849), the LB rate was 42.4% (360/849), and the overall miscarriage rate from hCG to live birth was 26.8% (132/492).The overall AUC for FH prediction was 0.652 (0.616 to 0.689) for iDAScore v2.0 compared to 0.632 (0.595 to 0.669) for v1.0. Similarly, the AUC for LB prediction was 0.644 (95% CI: 0.607 to 0.681) for iDAScore v2.0 compared to 0.626 (95% CI: 0.588 to 0.663) for iDAScore v1.0. However, these differences were not statistically significant (P-values: 0.12 for FH and 0.17 for LB) in paired comparisons.For miscarriage, we found an increase in AUC from v1.0 (0.574; 95% CI: 0.514 to 0.633) to v2.0 (0.592; 95% CI: 0.534 to 0.649), P-value: 0.40.

Limitations, reasons for caution
This study only includes data from a single clinic. Patient characteristics and procedures followed might not be representative for other clinics. The data is limited to blastocysts that were chosen for transfer by morphology from trained embryologists. Furthermore, the limited sample size does not provide statistically significant differences in performance.

Wider implications of the findings
It is important that predictive models are constantly updated and improved. Updated versions should take into account a larger database as well as potential new methodologies that evolve in the rapidly developing AI field. This study shows that the iDAScore model update seems to represent an actual performance increase.

Trial registration number
1-16-02-277-22