Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

We assessed the diagnostic utility of uric acid for the prediction of preeclampsia. An observational prospective approach was carried out during 2014. Preeclamptic women were classified into 4 groups accordingly to the onset of preeclampsia and the presence of intrauterine growth restriction (IUGR). Serum uric acid levels, urea, and creatinine were measured. Receiver operating curves (ROC) of the uric acid levels ratio (UAr) between a dosage before and after the 20th week of gestation were performed. One thousand two hundred and ninety-third pregnant women were enrolled in this study. Eight hundred ten had non-complicated pregnancies, 40 preeclampsia, 33 gestational hypertension, and 20 IUGR without preeclampsia. Uric acid significantly raised after 20 weeks of gestation in women who develop preeclampsia before 34 weeks (Group A) or in those who develop preeclampsia after 37 weeks associated with IUGR (Group C). In women who develop preeclampsia after 34 weeks without IUGR (Groups B and D), uric acid increased after the 30th week of gestation. In all groups, UAr was greater than 1.5. In gestational hypertension, UAr was superior to 1.5 toward the end of gestation, while in IUGR without preeclampsia, the behavior of serum uric acid was similar to non-complicated pregnancies. In all cases, urea and creatinine showed normal values, confirming that patients had no renal compromise. ROC area was 0.918 [95% confidence interval (CI): 0.858–0.979) for the preeclampsia group and 0.955 (95% CI: 0.908–1.000) for Group A. UAr at a cut-off point ≥1.5 had a very low positive predictive value, but a high negative predictive value of 99.5% for preeclampsia and it reached 100% for Group A. Thus, a UAr less than 1.5 may be a helpful parameter with a strong exclusion value and high sensitivity for those women who are not expected to develop preeclampsia. Additionally, this low-cost test would allow for better use of resources in developing countries.