Preterm birth remains the leading cause of neonatal morbidity and mortality worldwide, affecting approximately 13.4 million births annually. Despite advances in our understanding of risk factors, current clinical prediction methods have demonstrated limited accuracy in individual risk stratification. This narrative review examines the current landscape of artificial intelligence (AI) applications for preterm birth prediction and evaluates the methodological quality and clinical applicability across different data modalities. PubMed, Embase, and Web of Science were searched to develop and validate machine learning models for predicting spontaneous preterm births. AI approaches include electronic health record-based models, deep learning for ultrasound image analysis, cervical texture and radiomics feature extraction, elastography-derived parameters, and multi-omics integration using transformer architectures. Area under the receiver operating characteristic curve values range from 0.61 to 0.94 across modalities. However, the systematic reviews identified significant methodological limitations; 79% of the studies had a high risk of bias according to the PROBAST criteria, with a median transparent reporting of multivariable prediction model for individual prognosis or diagnosis (TRIPOD) adherence of only 49%. Common deficiencies include inadequate sample sizes, a lack of external validation, and failure to report calibration metrics. Although AI-based prediction shows promise, substantial improvements in methodological rigor are required before clinical implementation. Priority areas include rigorous external validation, adherence to TRIPOD+AI reporting standards, and prospective evaluation of clinical utility.
YooKyung Lee (Tue,) studied this question.