ArticleReproductive medicine and biology2019
Feasibility of deep learning for predicting live birth from 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 23 papers.
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Who cites it
23 citing papers in PubMed.
- Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.Nature medicine · 2024Trial
- Artificial Intelligence in Embryo Selection: Current Approaches and Clinical Implications.Bioengineering (Basel, Switzerland) · 2026Review
- Current progress and open challenges for applying artificial intelligence across thePatterns (New York, N.Y.) · 2025Review
- Article
- Quality of IVM ovarian tissue oocytes: impact of clinical, demographic, and laboratory factors.Journal of assisted reproduction and genetics · 2024Article
- Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.JMIR pediatrics and parenting · 2024Article
- A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning.Patterns (New York, N.Y.) · 2024Article
- Criteria for implementing artificial intelligence systems in reproductive medicine.Clinical and experimental reproductive medicine · 2024Article
- Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images.Scientific reports · 2024Article
- Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles.Reproductive biology and endocrinology : RB&E · 2024Article
- Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning.Journal of assisted reproduction and genetics · 2023Article
- Development and validation of deep learning based embryo selection across multiple days of transfer.Scientific reports · 2023Article
- Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study.Journal of assisted reproduction and genetics · 2022Article
- Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis.Biomedicines · 2022Review
- Embryo selection with artificial intelligence: how to evaluate and compare methods?Journal of assisted reproduction and genetics · 2021Article
- Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring.Communications biology · 2021Article
- Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service.JBRA assisted reproduction · 2020Review
- Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data.Journal of assisted reproduction and genetics · 2020Review
- Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types.Oncology letters · 2020Article
- Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images.Molecular and clinical oncology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation.
methodsA total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35-37, 38-39, 40-41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated.
resultsThe accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35-37, 38-39, 40-41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE (
conclusionsAI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.
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