Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.

The enterotoxigenic Escherichia coli strains lead to diarrhoea in humans due to heat-labile and heat-stable (STa) enterotoxins. STa increases Cl^-release in intestinal cells, including the human colonic carcinoma T₈₄ cell line, involving increased cGMP and membrane alkalization due to reduced Na⁺/H⁺ exchangers (NHEs) activity. Since NHEs modulate intracellular pH (pH_i), and NHE1, NHE2, and NHE4 are expressed in T₈₄ cells, we characterized the STa role as modulator of these exchangers. pH_i was assayed by the NH₄Cl pulse technique and measured by fluorescence microscopy in BCECF–preloaded cells. pH_i recovery rate (dpHi/dt) was determined in the absence or presence of 0.25 μmol/L STa (30 minutes), 25 μmol/L HOE-694 (concentration inhibiting NHE1 and NHE2), 500 μmol/L sodium nitroprusside (SNP, spontaneous nitric oxide donor), 100 μmol/L dibutyryl cyclic GMP (db-cGMP), 100 nmol/L H89 (protein kinase A inhibitor), or 10 μmol/L forskolin (adenylyl cyclase activator). cGMP and cAMP were measured in cell extracts by radioimmunoassay, and buffering capacity (ßi) and H⁺ efflux (J_H⁺) was determined. NHE4 protein abundance was determined by western blotting. STa and HOE-694 caused comparable reduction in dpHi/dt and J_H⁺ (~63%), without altering basal pH_i (range 7.144–7.172). STa did not alter ßi value in a range of 1.6 pH_i units. The dpHi/dt and J_H⁺ was almost abolished (~94% inhibition) by STa + HOE-694. STa effect was unaltered by db-cGMP or SNP. However, STa and forskolin increased cAMP level. STa–decreased dpHi/dt and J_H⁺ was mimicked by forskolin, and STa + HOE-694 effect was abolished by H89. Thus, incubation of T₈₄ cells with STa results in reduced NHE4 activity leading to a lower capacity of pH_i recovery requiring cAMP, but not cGMP. STa effect results in a causal phenomenon (STa/increased cAMP/increased PKA activity/reduced NHE4 activity) ending with intracellular acidification that could have consequences in the gastrointestinal cells function promoting human diarrhoea.

Background: Preeclampsia remains a leading cause of maternal morbidity worldwide. There is a critical need for predictive systems that not only perform accurately but also provide interpretable insights for clinical decision-making. This work introduces SK-MOEFS, an explainable framework based on fuzzy logic and multi-objective evolutionary optimization, designed to classify preeclampsia risk while generating clinically interpretable rules. Methods: The model integrates fuzzy decision trees with a genetic algorithm to identify a compact and relevant set of rules, optimized for both accuracy and interpretability. The system was trained and evaluated on third-trimester pregnancy data from a publicly available, multi-ethnic cohort comprising 574 individuals. All processes, including preprocessing, training, and evaluation, were conducted using open-source tools, ensuring reproducibility. Results: SK-MOEFS achieved 91% classification accuracy, an AUC of 0.89, and a recall of 0.88—outperforming other standard interpretable models while maintaining high transparency. The model emphasizes minimizing false negatives, which is critical in clinical risk stratification for preeclampsia. Conclusions: Beyond predictive performance, SK-MOEFS offers a rule translation and defuzzification layer that outputs probabilistic interpretations in natural language, enhancing its suitability for clinical use. This framework provides an effective bridge between algorithmic inference and human clinical judgment, supporting transparent and reliable decision-making in maternal care.