Abstract
Preterm birth (PTB) remains a significant challenge in modern obstetric practice, posing considerable risks to maternal and neonatal health. Despite advancements in medical technology, the incidence of PTB remains high, and its prediction continues to be complex. Traditional methods for predicting PTB, including medical history evaluation, cervical length measurement, and biochemical markers, have shown limited precision in preventing this serious complication. However, recent technological advancements-such as machine learning algorithms, biomarker profiling, and genetic analyses-offer new possibilities for improving prediction accuracy. This review critically examines current and emerging approaches for PTB prediction, highlighting their potential to transform early risk detection. It also addresses the ethical and societal implications of these technologies. This narrative review aims to comprehensively analyse contemporary methods for predicting preterm birth, evaluating established and emerging approaches. It will assess the efficacy of current predictive tools, examine the limitations they face, and explore the potential for integrating advanced technologies to improve outcomes. By highlighting recent developments in the field and addressing critical knowledge gaps, this review seeks to contribute to the ongoing effort to enhance PTB prediction, aiming to improve maternal and neonatal health outcomes. The study's novelty lies in its comprehensive analysis of cutting-edge innovations in PTB prediction and its focus on identifying critical gaps in current practices.