Correlation of fetal heartbeat outcome after Day 3 or Day 5 single embryo transfer of morphologically selected embryos with an annotation-free deep learning scoring system: Results from a multi-center study.

Abstract

Objective: To evaluate whether the use of a fully automated AI-based scoring system (iDAScore V2) for selecting viable embryos using fetal heartbeat (FHB) as an indicator is equivalent to morphology assessment.

Methods: A retrospective observational cohort study across four fertility centers analyzed embryos selected for single embryo transfer on Day 3 or Day 5 + based on morphology and time-lapse video. All transferred embryos from participating centers were retrospectively scored using a fully automated AI-based embryo scoring algorithm and standardized morphology assessment. The predictive ability of both methods for implantation (FHB rate) was compared for Day 3 and Day 5 + transfer.

Results: A multi-center analysis revealed that AI-based embryo scoring significantly outperformed morphological embryo assessment in predicting FHB for both Day 3 (n = 2965) and Day 5 + (n = 6970) transfers (P < 0.0001). Similarly, the discrimination of low versus high scores regarding FHB resulted in a significantly better area under the curve (AUC) for iDAScore V2 compared to standardized morphology assessment for Day 3 (0.63; 95% CI: 0.61-0.65 versus 0.59; 95% CI: 0.58-0.61) and for Day 5 + (0.59; 95% CI: 0.57-0.60 versus 0.55; 95% CI: 0.54-0.57).

Conclusions: As a multi-center validation of fully automated embryo assessment, this study confirms that AI-based selection provides outcomes that are either equivalent to or superior to morphological embryo assessment, without compromising clinical outcomes.