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Artificial intelligence technologies in predicting preeclampsia

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The strategy for preserving reproductive potential in the Russian Federation is focused on the personalized women’s health care and based on preclinical identification of gynecological diseases and major obstetric syndromes at the stage of predicting adverse outcomes and subsequent preventive measures able to reduce maternal and perinatal morbidity and mortality, decrease women and neonatal disability as well as profoundly reduce extremely high costs on care of premature infants. The search for effective
predictive methods of preeclampsia (PE) at the stage of preconception and in the first trimester of pregnancy is driven by the desire to identify women at greater risk of developing hypertensive disorders in order to take the necessary effective measures for
preventing placental pathology as early as possible, thereby preventing or reducing incidence rate of PE. At the same time, identifying a group of high-risk women would allow to rationally plan prenatal care, timely recognize emergence of multiple organ
dysfunction and immediately begin pathogenetic and symptomatic therapy. Taking into account the national and global experience of using predictive analytics software proving their success in reproductive medicine, it is reasonable to assume that converting prognosis into digital format by using artificial intelligence (AI) algorithms will open new opportunities for increasing accuracy of individual risk calculation for PE by meeting current paradigm of personalized preventive medicine. Our scientific review on domestic and international publications aims to inform a wide range of obstetricians-gynecologists about advances in AI technologies as well as prospects for machine learning to predict PE.

About the Authors

A. A. Ivshin
Petrozavodsk State University
Russian Federation

Alexander A. Ivshin – MD, PhD, Associate Professor, Acting Head of the Department of Obstetrics and Gynecology and Dermatovenerology 

33 Lenin Avenue, Petrozavodsk 185031

T. Z. Bagaudin
Petrozavodsk State University
Russian Federation

Tavat Z. Bagaudin – Student, Medical Institute 

33 Lenin Avenue, Petrozavodsk 185031

A. V. Gusev
K-Skay LLC
Russian Federation

Alexander V. Gusev – PhD (Engineering), Business Development Director 

17 Varkaus Embankment, Petrozavodsk 185901


1. Di Renzo G.C. The great obstetrical syndromes. J Matern Fetal Neonatal Med. 2009;22(8):633–5.

2. Brosens I., Pijnenborg R., Vervruysse L., Romero R. The «Great obstetrical syndromes» are associated with disorders of deep placentation. Am J Obstet Gynecol. 2011;204(3):193–201.

3. Mastrolia S.A., Mazor M., Loverro G. et al. Placental vascular pathology and increased thrombin generation as mechanisms of desease in obstetrical syndromes. Perr J. 2014;18(2):e653.

4. Walker J.J. Pre-eclampsia. Lancet. 2000;356(9237):1260–5.

5. Ghulmiyyah L., Sibai B. Maternal mortality from preeclampsia/eclampsia. Semin Perinatol. 2012;36(1):56–9.

6. Kuklina E.V., Ayala C., Callaghan W.M. Hypertensive disorders and severe obstetric morbidity in the United States. Obstet Gynecol. 2009;113(6):1299–306.

7. ACOG Committee Opinion No. 743: Low-Dose Aspirin Use During Pregnancy. Obstet Gynecol. 2018;132(1):e44–e52.

8. Rolnik D.L., Wright D., Poon L.C. et al. Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia. N Engl J Med. 2017;377(7):613–22.

9. Duley L., Meher S., Hunter K.E. et al. Antiplatelet agents for preventing pre-eclampsia and its complications. Cochrane Database Syst Rev. 2019;2019(10):CD004659.

10. Клинические рекомендации «Преэклампсия. Эклампсия. Отеки, протеинурия и гипертензивные расстройства во время беременности, в родах и послеродовом периоде». Минздрав России, 2021. 79 с.

11. Duhig K., Vandermolen B., Shennan A. Recent advances in the diagnosis and management of pre-eclampsia. F1000Res. 2018;7:242.

12. Schork N.J. Artificial intelligence and personalized medicine. Cancer Treat Res. 2019;178:265–83.

13. Camacho D.M., Collins K.M., Powers R.K. et al. Next-generation machine learning for biological networks. Cell. 2018;173(7):1581–92.

14. Sidey-Gibbons J., Sidey-Gibbons C. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64.

15. Ившин А.А., Гусев А.В., Новицкий Р.Э. Искусственный интеллект: предиктивная аналитика перинатального риска. Вопросы гинекологии, акушерства и перинатологии. 2020;19(6):133–44.

16. Ившин А.А., Багаудин Т.З., Гусев А.В. Искусственный интеллект на страже репродуктивного здоровья. Акушерство и гинекология. 2021;(5):17–24.

17. 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.

18. 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.

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. Marić I., Tsur A., Aghaeepour N. et al. Early prediction of preeclampsia via machine learning. Am J Obstet Gynecol MFM. 2020;2(2):100100.

28. Praciano de Souza P.C., Gurgel Alves J.A., Holanda Moura S. et al. Second trimester screening of preeclampsia using maternal characteristics and uterine and ophthalmic artery Doppler. Ultraschall Med. 2018;39(2):190–7.

29. Gomaa M.F., Naguib A.H., Swedan K.H., Abdellatif S.S. Serum tumor necrosis factor-α level and uterine artery Doppler indices at 11-13 weeks' gestation for preeclampsia screening in low-risk pregnancies: a prospective observational study. J Reprod Immunol. 2015;109:31–5.

30. Zhou J., Zhao X., Wang Z., Hu Y. Combination of lipids and uric acid in mid-second trimester can be used to predict adverse pregnancy outcomes. J Matern Fetal Neonatal Med. 2012;25(12):2633–8.

31. Jhee J.H., Lee S., Park Y. et al. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One. 2019;14(8):e0221202.

32. Wright D., Akolekar R., Syngelaki A. et al. A competing risks model in early screening for preeclampsia [published correction appears in Fetal Diagn Ther. 2013;34(1):18]. Fetal Diagn Ther. 2012;32(3):171–8.

33. Akolekar R., Syngelaki A., Poon L. et al. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers [published correction appears in Fetal Diagn Ther. 2013;34(1):43]. Fetal Diagn Ther. 2013;33(1):8–15.

34. Wright A., Wright D., Syngelaki A. et al. Two-stage screening for preterm preeclampsia at 11–13 weeks' gestation. Am J Obstet Gynecol. 2019;220(2):197.e1–197.e11.

35. Wright D., Tan M.Y., O'Gorman N. et al. Predictive performance of the competing risk model in screening for preeclampsia [published correction appears in Am J Obstet Gynecol. 2019 Apr 24]. Am J Obstet Gynecol. 2019;220(2):199.e1–199.e13.

36. Andrietti S., Silva M., Wright A. et al. Competing-risks model in screening for preeclampsia by maternal factors and biomarkers at 35-37 weeks' gestation. Ultrasound Obstet Gynecol. 2016;48(1):72–9.

37. Tan M.Y., Wright D., Syngelaki A. et al. Comparison of diagnostic accuracy of early screening for pre-eclampsia by NICE guidelines and a method combining maternal factors and biomarkers: results of SPREE. Ultrasound Obstet Gynecol. 2018;51(6):743–50.

38. Poon L.C., Rolnik D.L., Tan M.Y. et al. ASPRE trial: incidence of preterm pre-eclampsia in patients fulfilling ACOG and NICE criteria according to risk by FMF algorithm. Ultrasound Obstet Gynecol. 2018;51(6):738–42.

39. Sonek J., Krantz D., Carmichael J. et al. First-trimester screening for early and late preeclampsia using maternal characteristics, biomarkers, and estimated placental volume. Am J Obstet Gynecol. 2018;218(1):126.e1–126.e13.

For citation:

Ivshin A.A., Bagaudin T.Z., Gusev A.V. Artificial intelligence technologies in predicting preeclampsia. Obstetrics, Gynecology and Reproduction. 2021;15(5):576-585. (In Russ.)

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