ArticleF&S reports2022
Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics.
Article in F&S reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The Influencing Factors and Predictive Algorithm of Pregnancy Outcomes in IVF/ICSI-ET Patients.International journal of women's health · 2026Article
- Predicting pregnancy outcomes in IVF cycles: a systematic review and diagnostic meta- analysis of artificial intelligence in embryo assessment.Contraception and reproductive medicine · 2025Review
- Artificial Intelligence and Machine Learning: An Updated Systematic Review of Their Role in Obstetrics and Midwifery.Cureus · 2025Review
- Could cleaved embryo morphology and morphokinetics be associated with prenatal and neonatal outcomes?Journal of assisted reproduction and genetics · 2025Article
- Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.Mayo Clinic proceedings. Digital health · 2024Review
- Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency.Biology · 2024Review
- Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.Reproductive biology and endocrinology : RB&E · 2024Article
- Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.Journal of assisted reproduction and genetics · 2024Article
- Machine learning in time-lapse imaging to differentiate embryos from young vs old mice†.Biology of reproduction · 2024Article
- A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.Journal of ovarian research · 2024Article
- Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.Journal of assisted reproduction and genetics · 2024Review
- Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk.Bioengineering (Basel, Switzerland) · 2023Article
- A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems.Human reproduction (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Objective: To evaluate the feasibility of generating a center-specific embryo morphokinetic algorithm by time-lapse microscopy to predict clinical pregnancy rates. Design: A retrospective cohort analysis. Setting: Academic fertility clinic in a tertiary hospital setting. Patients: Patients who underwent in vitro fertilization with embryos that underwent EmbryoScope time-lapse microscopy and subsequent transfer between 2014 and 2018. Interventions: None. Main Outcome Measures: Clinical pregnancy. Results: A supervised, random forest learning algorithm from 367 embryos successfully predicted clinical pregnancy from a training set with overall 65% sensitivity and 74% positive predictive value, with an area under the curve of 0.7 for the test set. Similar results were achieved for live birth outcomes. For the secondary analysis, embryo growth morphokinetics were grouped into five clusters using unsupervised clustering. The clusters that had the fastest morphokinetics (time to blastocyst = 97 hours) had pregnancy rates of 54%, whereas a cluster that had the slowest morphokinetics (time to blastocyst = 122 hours) had a pregnancy rate of 71%, although the differences were not statistically significant ( Conclusions: This study shows the feasibility of a clinic-specific, noninvasive embryo morphokinetic simple machine learning model to predict clinical pregnancy rates.
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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.