akusherstvoObstetrics, Gynecology and ReproductionАкушерство, Гинекология и Репродукция2313-73472500-3194IRBIS LLC10.17749/2313-7347/ob.gyn.rep.2021.229akusherstvo-1047Research ArticleREVIEW ARTICLESНАУЧНЫЕ ОБЗОРЫArtificial intelligence technologies in predicting preeclampsiaПрогнозирование преэклампсии с использованием технологий искусственного интеллектаhttps://orcid.org/0000-0001-7834-096XИвшинА. А.IvshinA. A.

Ившин Александр Анатольевич – кандидат медицинских наук, доцент, исполняющий обязанности  заведующего кафедрой акушерства и гинекологии, дерматовенерологии медицинского института 

185031 Петрозаводск, проспект Ленина, д. 33

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

33 Lenin Avenue, Petrozavodsk 185031

scipeople@mail.ruhttps://orcid.org/0000-0002-0603-3570БагаудинТ. З.BagaudinT. Z.

Багаудин Тават Зулкаидовна – студент медицинского института 

185031 Петрозаводск, проспект Ленина, д. 33

Tavat Z. Bagaudin – Student, Medical Institute 

33 Lenin Avenue, Petrozavodsk 185031

https://orcid.org/0000-0002-7380-8460ГусевА. В.GusevA. V.

Гусев Александр Владимирович – кандидат технических наук, директор по развитию бизнеса 

185910 Петрозаводск, набережная Варкауса, д. 17

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

17 Varkaus Embankment, Petrozavodsk 185901

ФГБОУ ВО «Петрозаводский государственный университет»РоссияPetrozavodsk State UniversityRussian FederationООО «К-Скай»РоссияK-Skay LLCRussian Federation202126082021155576585Copyright © Ivshin A.A., Bagaudin T.Z., Gusev A.V., 20212021Ившин А.А., Багаудин Т.З., Гусев А.В.Ivshin A.A., Bagaudin T.Z., Gusev A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.gynecology.su/jour/article/view/1047

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 effectivepredictive 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 forpreventing 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 organdysfunction 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.

Стратегия сохранения репродуктивного потенциала Российской Федерации сфокусирована на персонифицированной охране здоровья женщины и основана на доклиническом выявлении гинекологических заболеваний и больших акушерских синдромов на этапе предикции неблагоприятных исходов и последующих превентивных мероприятиях, способных уменьшить материнскую и перинатальную заболеваемость и смертность, снизить инвалидизацию женщин и новорожденных, существенно сократить при этом чрезвычайно высокие расходы на лечение недоношенных. Поиски путей эффективного прогнозирования преэклампсии (ПЭ) на этапе преконцепции и в I триместре беременности вызваны стремлением выявить женщин с высоким риском развития гипертензивных расстройств с целью как можно раньше принять необходимые эффективные меры профилактики патологии плацентации и таким образом предотвратить или уменьшить частоту возникновения ПЭ. Вместе с тем выявление беременных группы высокого риска позволит рационально спланировать дородовое наблюдение, своевременно распознать возникновение полиорганной дисфункции и немедленно приступить к патогенетической терапии. Принимая во внимание отечественный и мировой опыт использования интегральных систем предиктивной аналитики, доказывающий их эффективность в репродуктивной медицине, логично предположить, что конверсия прогнозирования в цифровой формат с использованием алгоритмов искусственного интеллекта (ИИ) откроет новые возможности для повышения точности расчета индивидуального риска ПЭ, отвечая современной парадигме персонифицированной профилактической медицины. Представленный научный обзор отечественной и зарубежной литературы имеет своей целью информирование широкого круга специалистов в области акушерства и гинекологии о достижениях технологий ИИ и перспективах машинного обучения в прогнозировании ПЭ.

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The authors declare that there are no conflicts of interest present.