ArticleReproductive medicine and biology
Predicting implantation by using dual AI system incorporating three-dimensional blastocyst image and conventional embryo evaluation parameters-A pilot study.
Article in Reproductive medicine and biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed.
- Characteristics of fetal facial expression changes using artificial intelligence: a pilot study.Scientific reports · 2025Article
- Predicting implantation by using dual AI system incorporating three-dimensional blastocyst image and conventional embryo evaluation parameters-A pilot study.Reproductive medicine and biologyArticle
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Authors and funding
4 authors.
Funding
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Abstract
Purpose: To investigate the usefulness of an original dual artificial intelligence (AI) system, in which the first AI system eliminates the background of sliced tomographic blastocyst images, then the second AI system predicts implantation success using three-dimensional (3D) reconstructed images of the sequential images and conventional embryo evaluation parameters (CEE) such as maternal age. Methods: Patients (from June 2022 to July 2023) could opt out and there was additional information on the Web site of the clinic. Implantation and non-implantation cases numbered 458 and 519, respectively. A total of 10 747 tomographic images of the blastocyst in a time-lapse incubator system with the CEE were obtained. Results: The statistic values by the dual AI system were 0.774 ± 0.033 (mean ± standard error) for area under the characteristic curve, 0.727 for sensitivity, 0.719 for specificity, 0.727 for predictive value of positive test, 0.719 predictive value of negative test, and 0.723 for accuracy, respectively. Conclusions: The usefulness of the dual AI system in predicting implantation of blastocyst in handling 3D data with conventional embryo evaluation information was demonstrated. This system may be a feasible option in clinical practice.
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