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Related Experiment Videos

Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation.

Daichi Inoue1

  • 1Nishitan-Art Clinic Nagoya-Ekimae, Meieki 3-28-12, Dainagoya Building 8F, Nakamura-ku, Nagoya 450-6408, Japan.

Journal of Clinical Medicine
|July 15, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise for predicting miscarriage and recurrent pregnancy loss (RPL) by analyzing complex reproductive data. However, AI

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Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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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Area of Science:

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

Background:

  • Recurrent pregnancy loss (RPL) affects many, with numerous cases remaining unexplained by current methods.
  • Traditional statistical models struggle with complex interactions among clinical, imaging, and molecular factors in pregnancy loss.
  • Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers a novel framework for risk prediction and patient stratification in reproductive medicine.

Purpose of the Study:

  • To review current evidence on AI-based prediction of miscarriage and RPL.
  • To explore the translational relevance of AI in infertility treatment and assisted reproductive technology (ART).
  • To identify limitations and future directions for AI in reproductive health.

Main Methods:

Keywords:
IVFartificial intelligenceassisted reproductive technologyembryo selectionendometrial receptivityinfertilitymachine learningmiscarriageradiomicsrecurrent pregnancy loss

Related Experiment Videos

  • Narrative review of existing literature on AI applications in miscarriage and RPL prediction.
  • Analysis of clinical data-driven models, biomarker-integrated ML approaches, and imaging-based AI (radiomics).
  • Examination of AI's role in assisted reproductive technology (ART) processes like embryo selection and laboratory workflow.

Main Results:

  • Clinical data-driven AI models demonstrate potential in discriminating risk for pregnancy loss.
  • Biomarker-integrated ML suggests immune-inflammatory signatures are relevant for risk estimation.
  • Imaging-based AI shows promise for non-invasive assessment of endometrial receptivity and guiding embryo transfer.

Conclusions:

  • AI holds potential for personalized reproductive care, aiding in risk prediction and patient stratification for miscarriage and RPL.
  • Current evidence is limited by study design, data size, inconsistent definitions, and validation issues.
  • Further research focusing on multimodal, explainable, and prospectively validated AI systems is crucial before routine clinical implementation.