AUTHOR=Xia Jie , Chen Chen , Lu Xiuqin , Zhang Tengfei , Wang Tingting , Wang Qingling , Zhou Qianqian 

TITLE=Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1631705

DOI=10.3389/fpubh.2025.1631705

ISSN=2296-2565

ABSTRACT=IntroductionPostpartum depression (PPD) is a significant mental health concern affecting 3.5-33.0% of mothers worldwide, with potentially severe consequences for both maternal and infant well-being. The emergence of artificial intelligence (AI) and machine learning (ML) technologies offers new opportunities for the early prediction of PPD risk, potentially enabling timely interventions to mitigate adverse outcomes.MethodsThis systematic review was conducted until October 31, 2024, using several electronic databases, including PubMed, Web of Science, CBM, VIP, CNKI, and Wanfang Data. All the studies predicted the occurrence of PPD using algorithms. The review process involved dual-independent screening by two authors using predefined criteria, with discrepancies resolved through consensus discussion involving a third investigator, and assessed the quality of the included models using the prediction model risk of bias assessment tool (PROBAST). Inter-rater agreement was quantified using Cohen’s κ.ResultsEleven studies were included in the systematic review. The random forest, support vector machine, and logistic regression algorithms demonstrated high predictive performance (AUROC > 0.9). The main predictors of PPD were maternal age, pregnancy stress and adverse emotions, history of mental disorders, maternal education, marital relationship, and sleep status. The overall performance of the prediction model was excellent. However, the generalizability of the model was limited, and there was a certain risk of bias. Issues such as data quality, algorithm interpretability, and the cross-cultural and cross-population applicability of the model need to be addressed.ConclusionThe model has the potential to predict the risk of PPD and provide support for early identification and intervention. Future research should optimize the model, improve its prediction accuracy, and test its applicability across cultures and populations to reduce the incidence of PPD and guarantee the mental health of pregnant and maternal women.