Publications by authors named "L Bori"
Background: In recent times, various algorithms have been developed to assist in the selection of embryos for transfer based on artificial intelligence (AI). Nevertheless, the majority of AI models employed in this context were characterized by a lack of transparency. To address these concerns, we aim to design an interpretable tool to automate human embryo evaluation by combining artificial neural networks (ANNs) and genetic algorithms (GA).
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- The study aimed to determine if segmental aneuploid embryos exhibited distinct morphokinetic patterns compared to euploid and whole-chromosome aneuploid embryos during development.
- Researchers analyzed data from over 7,000 embryos cultured in European IVF clinics and found that segmental aneuploids had significantly slower cleavage rates, particularly during the first three cell cycles.
- A logistic regression model was developed to predict aneuploidy types based on morphokinetic data, but its overall predictive performance was modest when tested on new data.
Objective: To externally validate a fully automated embryo classification system for in vitro fertilization (IVF) treatments.
Design: Retrospective cohort study.
Setting: Clinic.
Study Question: Could an artificial intelligence (AI) algorithm predict fetal heartbeat from images of vitrified-warmed embryos?
Summary Answer: Applying AI to vitrified-warmed blastocysts may help predict which ones will result in implantation failure early enough to thaw another.
What Is Known Already: The application of AI in the field of embryology has already proven effective in assessing the quality of fresh embryos. Therefore, it could also be useful to predict the outcome of frozen embryo transfers, some of which do not recover their pre-vitrification volume, collapse, or degenerate after warming without prior evidence.
Objective: To compare the effect of a fully undisturbed culture strategy over a sequential one on embryo in vitro development and clinical outcomes in intracytoplasmic sperm injection (ICSI) cycles.
Design: Retrospective cohort study.
Setting: University-affiliated private IVF center.