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Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions.

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Artificial intelligence (AI) and machine learning (ML) can analyze complex data to uncover causes of recurrent pregnancy loss (RPL). However, challenges like data limitations must be overcome for clinical use.

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Genetics and Immunology

Background:

  • Nearly half of recurrent pregnancy loss (RPL) cases lack a known cause, indicating limitations in current diagnostic methods.
  • There is a need for advanced approaches to detect subtle biological patterns contributing to RPL.
  • Genetics, immunology, and endometrial research have advanced but haven't fully explained RPL etiology.

Purpose of the Study:

  • To review and assess the impact of artificial intelligence (AI) on understanding, predicting, and managing RPL.
  • To focus on machine learning (ML) for identifying latent biological pathways in pregnancy loss.
  • To explore AI's role in understanding multifactorial contributors to RPL.

Main Methods:

  • A narrative review of current research on AI applications in reproductive medicine.
  • Analysis of studies using imaging, proteomic, genomic, clinical, and multi-omics data for RPL prediction.
  • Inclusion of research developing predictive or mechanistic models for RPL using AI.

Main Results:

  • AI effectively detects complex interactions between environmental, immunological, biochemical, and genetic factors in RPL.
  • ML and deep learning (DL) improve prognostic accuracy and identify novel biomarkers for RPL.
  • AI facilitates personalized reproductive profiles by integrating diverse data, enhancing prediction and counseling.

Conclusions:

  • AI shows potential for personalized RPL prediction and improved mechanistic understanding.
  • Clinical translation of AI in RPL is hindered by small datasets, definition conflicts, and validation issues.
  • Addressing limitations like data diversity and prospective trials is crucial for integrating AI into RPL care.