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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

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In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: Sep 13, 2025

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An integrated optimization and deep learning pipeline for predicting live birth success in IVF using feature

Arezoo Borji1, Hossam Haick2, Birgit Pohn3

  • 1Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria; Department of Medicine, Danube Private University (DPU), Krems, Austria; Department of Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Computer Methods and Programs in Biomedicine
|July 30, 2025
PubMed
Summary

This study developed an artificial intelligence (AI) pipeline to accurately predict live birth outcomes in in vitro fertilization (IVF) treatments. The AI model achieved 97% accuracy, offering potential for personalized fertility care.

Keywords:
Feature SelectionIn-vitro-fertilization (ivf)Live birth successParticle swarm optimization (pso)Tab_transformerTransformer-based model

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Data Science

Background:

  • Predicting in vitro fertilization (IVF) success is challenging due to complex clinical, demographic, and procedural factors.
  • Accurate prediction of live birth outcomes is crucial for optimizing assisted reproductive technology treatments.

Purpose of the Study:

  • To develop a highly accurate artificial intelligence (AI) pipeline for predicting live birth outcomes in IVF.
  • To enhance the interpretability of AI models used in fertility treatment prediction.

Main Methods:

  • Evaluated various feature selection methods (PCA, PSO) and machine learning classifiers (RF, Decision Tree, Transformer, Tab_transformer).
  • Analyzed confounding factors (age, previous cycles) and preprocessing techniques.
  • Employed Shapley Additive Explanations (SHAP) for model interpretability.

Main Results:

  • The combination of Particle Swarm Optimization (PSO) for feature selection and a Tab_transformer deep learning model achieved 97% accuracy and 98.4% AUC.
  • SHAP analysis identified key predictors of infertility and improved model interpretability.

Conclusions:

  • Developed a robust AI pipeline for predicting IVF live birth outcomes with high accuracy and interpretability.
  • The AI pipeline shows potential for enhancing personalized fertility treatments and improving patient care.