ArticleBMC pregnancy and childbirth2022
Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.
Article in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.Frontiers in reproductive health · 2025Pooled it
- Study of comparative performance of general-purpose LLM-based systems in predicting IVF outcomes.Journal of assisted reproduction and genetics · 2026Article
- Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Breaking barriers in male infertility: the power of artificial intelligence-driven solutions.Frontiers in urology · 2026Review
- Federated task-adaptive learning for personalized selection of human IVF-derived embryos.Communications medicine · 2025Article
- [Progress in the application of artificial intelligence in assisted reproduction].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Review
- The Revolution in Midwifery Education: How AI and Deep Learning are Transforming Outcome-Based Assessments?Advances in medical education and practice · 2025Review
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Introducing artificial intelligence and sperm epigenetics in the fertility clinic: a novel foundation for diagnostics and prediction modelling.Frontiers in reproductive health · 2025Article
- Analysis of factors influencing clinical pregnancy rates in frozen-thawed embryo transfer cycles.Frontiers in endocrinology · 2025Article
- Application of artificial intelligence in VSD prenatal diagnosis from fetal heart ultrasound images.BMC pregnancy and childbirth · 2024Article
- Article
- Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer.Reproductive biology and endocrinology : RB&E · 2024Article
- Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.Reproductive biology and endocrinology : RB&E · 2024Article
- Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†.Biology of reproduction · 2024Article
- Machine learning for predicting elective fertility preservation outcomes.Scientific reports · 2024Article
- Embryo Selection in the Context of In Vitro Fertilization.The Linacre quarterly · 2024Article
- An artificial intelligence algorithm to select most viable embryos considering current process in IVF labs.Frontiers in artificial intelligence · 2024Article
- Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.Biology methods & protocols · 2024Article
- Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation.Journal of assisted reproduction and genetics · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system?
methodsThis study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
resultsThe deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation.
conclusionsThis research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient's embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.