Stillbirth Prediction: Current Approaches, Challenges, and Future Directions

Current approaches to stillbirth prediction are limited by datasets with a lack of clinical granularity, the restraints of traditional regression-based modeling, and inability to simultaneously account for the risks of stillbirth and of postnatal mortality. While external validation of risk stratification models in diverse populations remains a challenge, novel technologies show promise in addressing current limitations. Omics-based technologies and artificial intelligence-based approaches offer an avenue to improve mechanistic understanding and clinical prognostication. Novel approaches still require external validation and evaluation in clinical studies prior to implementation.