Objective: Postpartum depression is a frequent complication after childbirth, affecting maternal health, infant development, and family well-being. This study evaluated the role of artificial intelligence (AI) in preventing and detecting postpartum depression early. Methods: A systematic search was conducted in Scopus, PubMed, Web of Science, and CINAHL for studies (2020–2025) applying AI to identify postpartum depression. PRISMA guidelines guided selection and appraisal. Two random-effects meta-analyses estimated pooled sensitivity and accuracy based on total sample size and reported metrics. Results: Of 1,857 records, 16 studies met inclusion criteria. Machine learning models (Random Forest, XGBoost, neural networks) showed greater accuracy than traditional methods. Integration of AI with medical records and social media data enabled earlier, personalized detection. Reported challenges included algorithmic bias, data privacy, and implementation barriers. Pooled sensitivity was 69% (95% CI: 55–81%; n=277,496) and accuracy 79% (95% CI: 73–85%; n=306,156). Conclusions: AI shows promise for enhancing postpartum depression detection and prevention but requires addressing ethical, technical, and educational challenges to achieve equitable clinical integration. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251004175 , identifier CRD420251004175.

Background/Objectives: Diet, physical activity, and sleep have been proposed as modulators of endometriosis, yet most evidence comes from studies in which cases are much older than controls, confounding lifestyle comparisons by age. We compared these profiles against controls of closer reproductive age and, as a secondary aim, explored how far self-reported data discriminate endometriosis. Methods: Cross-sectional case-control study of 177 women: 77 with self-reported, procedure-confirmed endometriosis recruited through a patient association and 100 controls consecutively enrolled from the general adult population. Measures included Mediterranean diet adherence (PREDIMED-14), physical activity (IPAQ), pain scales, menstrual quality of life (CVM-22) and the Epworth Sleepiness Scale. Analyses used age-adjusted logistic regression with Benjamini–Hochberg correction; three machine learning algorithms were evaluated with cross-validation. Results: Mediterranean diet adherence and physical activity did not differ between groups; body mass index was modestly lower in cases, and daytime sleepiness was marginally higher but not after age adjustment. Of the 13 testable dietary items, only sofrito (vegetables slowly cooked in extra-virgin olive oil) survived correction, being reported less often by cases. Menstrual quality of life was markedly worse in cases. Discrimination (best AUC-ROC 0.933) reflected concurrent symptoms rather than nutrition: restricted to antecedent and dietary variables it reached only 0.67–0.73. Conclusions: Once age and multiple testing were accounted for, diet and physical activity did not distinguish women with endometriosis from controls. The lower sofrito intake is a single-item, cross-sectional finding that should be regarded as hypothesis-generating and requires independent validation.

Female pubic hair removal is a widespread practice shaped by aesthetic, cultural, and hygienic factors, with rising global prevalence, particularly in West