Machine learning in placental research: a review and guide for future studies

Machine learning approaches are increasingly applied across biomedical research to analyze complex, high-dimensional datasets and identify patterns that may inform disease diagnosis and biological discovery. In reproductive and placental research, these methods are used to investigate fetal development, pregnancy complications, and early-life health outcomes using molecular and clinical data. Machine learning approaches have widely been applied to placental and obstetric imaging, such as ultrasound, although these applications have been extensively reviewed elsewhere. Despite growing interest, many placental machine learning studies face recurring methodological challenges that limit interpretability and translational relevance. In this review, we summarize common machine learning approaches and concepts relevant to applying machine learning to placental biology. We then examine emerging applications of machine learning in placental research across genomics, epigenomics, transcriptomics, and proteomics. We propose best practices for designing robust and reproducible machine learning studies in placental research and highlight future directions, emphasizing prospective prediction and data sharing to support clinically meaningful translation.