ArticleFrontiers in reproductive health2026
Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.
Article in Frontiers in reproductive health, 2026. 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.
- Artificial Intelligence-Driven Reproductive Bioengineering: Integrating Fertility Diagnostics, Organ-on-Chip Systems, Cryobiology and Epigenetic Safety for Precision Reproductive Medicine.Bioengineering (Basel, Switzerland) · 2026Review
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8 authors.
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Abstract
Introduction: Artificial intelligence tools show promise in supporting clinical decision making, but their safe use requires evaluation of not only accuracy, but also agreement with experts and interpretability of model decisions. The aim of this study was to evaluate the accuracy and agreement of human embryologists and deep learning models in embryo stage classification, and to explore interpretability through explainable artificial intelligence. Methods: A retrospective, single-center study used single-frame embryo images ( Results: Embryologists achieved higher accuracy (89.9%) than ResNet-34 (78.8%, Conclusions: These findings highlight the need for evaluation frameworks that integrate accuracy, agreement and interpretability to support safe and transparent development of artificial intelligence tools in assisted reproduction technology.
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