Ключевые слова
Аннотация
Preeclampsia is a multifactorial obstetric complication that occurs in the second half of pregnancy and is characterized by hypertension and dysfunction of various organs, significantly increasing the risk of maternal and perinatal morbidity and mortality.
Despite considerable progress in understanding the pathophysiological mechanisms, effective methods for early detection and prediction of preeclampsia remain a pressing issue in clinical practice. This article presents an overview of modern approaches to predicting preeclampsia using artificial intelligence (AI) technologies. Machine learning algorithms, including random forests, gradient boosting, and deep neural networks, are discussed in the context of analyzing clinical, biochemical, and ultrasound data of pregnant women. Particular attention is given to the integration of multimodal data to improve prediction accuracy.
Библиографические ссылки
National Collaborating Centre for Women's and Children's Health (UK).
Hypertension in Pregnancy: The Management of Hypertensive Disorders During Pregnancy. London: RCOG Press, 2010.
LeFevre M.L., U.S. Preventive Services Task Force. Low-dose aspirin use for the prevention of morbidity and mortality from preeclampsia: U.S.
Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;161(11):819–26. https://doi.org/10.7326/M14-1884.
Poon L.C., Shennan A., Hyett J.A. et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention [published correction appears in Int J Gynaecol Obstet. 2019;146(3):390–1]. Int J Gynaecol Obstet. 2019;145(Suppl 1):1–33. https://doi.org/10.1002/ijgo.12802.
O’Gorman N., Wright D., Poon L.C. et al. Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks’ gestation: comparison with NICE guidelines and ACOG recommendations.
Ultrasound Obstet Gynecol. 2017;49(6):756–60. https://doi.org/10.1002/ uog.17455.
Kleinrouweler C.E., Cheong-See F.M., Collins G.S. et al. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet
Gynecol. 2016;214(1):79–90.e36. https://doi.org/10.1016/j. ajog.2015.06.013. 6. Kenny L.C., Dunn W.B., Ellis D.I. et al. Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics.
2005;1(3):227–34. https://doi.org/10.1007/s11306-005-0003-1. van Kuijk S.M., Delahaije D.H., Dirksen C.D. et al. External validation of a model for periconceptional prediction of recurrent early-onset preeclampsia. Hypertens Pregnancy. 2014;33(3):265–76. https://doi.org/ 10.3109/10641955.013.872253.
Villa P.M., Marttinen P., Gillberg J. et al. Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of
Preeclampsia and Intrauterine Growth Restriction (PREDO) study. PLoS One. 2017;12(3):e0174399. https://doi.org/10.1371/journal. pone.0174399.
Tejera E., Areias J.M., Rodrigues A. et al. Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes. J Matern Fetal Neonatal Med.
2011;24(9):1147–51. https://doi.org/10.3109/14767058.2010.545916.
Neocleous C.K., Anastasopoulos P., Nikolaides K.H. et al. Neural networks to estimate the risk for preeclampsia occurrence. International Joint
Conference on Neural Networks. Atlanta, Georgia: USA. 14–19 June 2009.
2221–5. https://doi.org/10.1109/IJCNN.2009.5178820.
Marić I., Tsur A., Aghaeepour N. et al. Early prediction of preeclampsia via machine learning. Am J Obstet Gynecol MFM. 2020;2(2):100100.