ArticleReproductive medicine and biology2019
Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age.
Article in Reproductive medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Characteristics of fetal facial expression changes using artificial intelligence: a pilot study.Scientific reports · 2025Article
- 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.Journal of assisted reproduction and genetics · 2025Observational
- Predicting pregnancy outcomes in IVF cycles: a systematic review and diagnostic meta- analysis of artificial intelligence in embryo assessment.Contraception and reproductive medicine · 2025Review
- Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos.Scientific reports · 2025Article
- Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.Reproductive biology and endocrinology : RB&E · 2024Article
- Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.JMIR pediatrics and parenting · 2024Article
- Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.Biology methods & protocols · 2024Article
- Artificial intelligence-the future is now.Journal of assisted reproduction and genetics · 2021Article
- Embryo selection with artificial intelligence: how to evaluate and compare methods?Journal of assisted reproduction and genetics · 2021Article
- Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types.Oncology letters · 2020Article
- Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age.Reproductive medicine and biology · 2019Article
- 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.
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Abstract
purposeTo identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age.
methodsRetrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated.
resultsThe values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35-37, 38-39, 40-41, and ≥42 years old, respectively.
conclusionsThough there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best.
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