Developing and Validating a Machine Learning Approach for Prediction of Euploid Pregnancy Loss in the Copenhagen Pregnancy Loss Study

This study represents the first machine learning approach to distinguish euploid from aneuploid pregnancy losses using clinical data alone, providing a framework for earlier recognition of women with treatable conditions before multiple losses occur. Novel associations between vitamin supplementation and ploidy status invite further mechanistic investigation. However, moderate discriminative performance and population-specific factors highlight the need for additional validation and exploration before clinical integration.