Observational studyJournal of assisted reproduction and genetics2025
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.
Observational study in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
10 authors.
Funding
Abstract
objectiveTo 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.
methodsA 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.
resultsA 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).
conclusionsAs 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.
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