akusherstvoObstetrics, Gynecology and ReproductionАкушерство, Гинекология и Репродукция2313-73472500-3194IRBIS LLC10.17749/2313-7347/ob.gyn.rep.2026.721akusherstvo-2818Research ArticleREVIEW ARTICLEНАУЧНЫЙ ОБЗОРApplication of artificial intelligence models trained on multiparametric datasets for predicting pregnancy complicationsПрименение моделей искусственного интеллекта на мультипараметрических наборах данных для прогнозирования осложнений беременностиhttps://orcid.org/0009-0008-0309-028XАтамасоваВ. А.AtamasovaV. A.

Атамасова Виктория Алексеевна 

420012 Казань, ул. Бутлерова, д. 49

Viktoriya A. Atamasova

420012 Kazan, Butlerova Street, 49

atamasovavika11@gmail.comhttps://orcid.org/0009-0002-5761-6669ХабибуллинаС. Р.KhabibullinaS. R.

Хабибуллина Сафия Рустемовна

420012 Казань, ул. Бутлерова, д. 49

Safiya R. Khabibullina

420012 Kazan, Butlerova Street, 49

https://orcid.org/0009-0002-3551-9825ФрумкинаЮ. П.FrumkinaYu. P.

Фрумкина Юлия Павловна

119048 Москва, ул. Трубецкая, д. 8, стр. 2

Yulia P. Frumkina

8 bldg. 2, Trubetskaya Str., Moscow 119048

https://orcid.org/0009-0000-0831-7123СоколоваЕ. И.SokolovaE. I.

Соколова Евгения Ивановна

117513 Москва, ул. Островитянова, д. 1

Evgeniia I. Sokolova

1 Ostrovityanova Str., Moscow 117513

https://orcid.org/0009-0002-8840-997XВолощукА. А.VoloshukA. A.

Волощук Алина Александровна

355017 Ставрополь, ул. Мира, д. 310

Alina A. Voloshuk

310 Mira Str., Stavropol 355017

https://orcid.org/0009-0009-8102-7128ШишенковаЕ. Г.ShishenkovaE. G.

Шишенкова Елизавета Глебовна

119048 Москва, ул. Трубецкая, д. 8, стр. 2

Elizaveta G. Shishenkova

8 bldg. 2, Trubetskaya Str., Moscow 119048

https://orcid.org/0009-0007-6699-6938БорисоваА. В.BorisovaA. V.

Борисова Алиса Владимировна

197022 Санкт-Петербург, ул. Льва Толстого, д. 6–8

Alisa V. Borisova

6–8 Lev Tolstoy Str., Saint Petersburg 197022

https://orcid.org/0009-0008-8695-074XВоронинаП. Ю.VoroninaP. Yu.

Воронина Полина Юрьевна

119048 Москва, ул. Трубецкая, д. 8, стр. 2

Polina Yu. Voronina

8 bldg. 2, Trubetskaya Str., Moscow 119048

ФГБОУ ВО «Казанский государственный медицинский университет» Министерства здравоохранения Российской ФедерацииРоссияKazan State Medical University, Ministry of Health of the Russian FederationRussian FederationФГАОУ ВО Первый Московский государственный медицинский университет имени И.М. Сеченова Министерства здравоохранения Российской Федерации (Сеченовский университет)РоссияSechenov UniversityRussian FederationФГАОУ ВО «Российский национальный исследовательский медицинский университет имени Н.И. Пирогова» Министерства здравоохранения Российской ФедерацииРоссияPirogov Russian National Research Medical University, Ministry of Health of the Russian FederationRussian FederationФГБОУ ВО «Ставропольский государственный медицинский университет» Министерства здравоохранения Российской ФедерацииРоссияStavropol State Medical University, Ministry of Health of the Russian FederationRussian FederationФГБОУ ВО «Первый Санкт-Петербургский государственный медицинский университет имени академика И.П. Павлова» Министерства здравоохранения Российской ФедерацииРоссияPavlov First Saint Petersburg State Medical University, Ministry of Health of the Russian FederationRussian Federation20201805202600Online First2818Copyright © Atamasova V.A., Khabibullina S.R., Frumkina Y.P., Sokolova E.I., Voloshuk A.A., Shishenkova E.G., Borisova A.V., Voronina P.Y., 20202020Атамасова В.А., Хабибуллина С.Р., Фрумкина Ю.П., Соколова Е.И., Волощук А.А., Шишенкова Е.Г., Борисова А.В., Воронина П.Ю.Atamasova V.A., Khabibullina S.R., Frumkina Y.P., Sokolova E.I., Voloshuk A.A., Shishenkova E.G., Borisova A.V., Voronina P.Y.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/2818Aim

Aim: to compare the predictive performance of artificial intelligence (AI) and machine learning (ML) models trained on multiparametric datasets for the prediction of preeclampsia (РЕ) and obstetric hemorrhage.

Materials and Methods

Materials and Methods. This systematic review was conducted in accordance with the PRISMA guidelines. PubMed and Cochrane Central databases were searched for studies published between 2015 and 2025. We included studies applying AI/ML with ≥ 2 predictors/data modalities and reporting outcomes related to РЕ/eclampsia or obstetric hemorrhage (including postpartum hemorrhage). Risk of bias was assessed using Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool and the Newcastle–Ottawa Scale (NOS).

Results

Results. Twenty-eight studies were included (18 on РЕ and 10 on hemorrhage). Most common algorithms were gradient boosting methods, random forests, XGBoost, and neural networks were the most common algorithms. For preeclampsia, stronger performance was more consistently reported when maternal risk factors were combined with blood pressure features and first-trimester screening components (uterine artery Doppler and placental biomarkers). For hemorrhage prediction, models based on electronic health records and preoperative clinical and laboratory variables, including risk stratification in placenta previa/placenta accreta spectrum (PAS), appeared particularly relevant. The evidence base is limited by predominantly retrospective designs and insufficient external/prospective validation, which undermines model transportability across settings and over time.

Conclusion

Conclusion. AI supported by multiparametric monitoring shows promise for predicting РЕ and obstetric hemorrhage; however, real-world implementation requires standardized reporting, external validation, and ongoing calibration monitoring.

Цель

Цель: провести сравнительный анализ прогностической эффективности моделей искусственного интеллекта (ИИ) и машинного обучения (МО), разработанных на мультипараметрических наборах данных, для прогнозирования преэклампсии (ПЭ) и акушерских кровотечений.

Материалы и методы

Материалы и методы. Систематический обзор выполнен согласно PRISMA. Поиск проведен в PubMed и Cochrane Central за 2015–2025 гг. Включались исследования с ИИ/МО, использующие ≥ 2 предикторов/классов данных и оценивающие ПЭ/эклампсию или акушерские кровотечения (включая послеродовые). Риск систематической ошибки оценивали с помощью инструмента ROBINS-I (англ. Risk Of Bias In Non-randomized Studies of Interventions; риск систематической ошибки в нерандомизированных исследованиях) и шкалы Ньюкасл–Оттава (англ. Newcastle–Ottawa Scale, NOS).

Результаты

Результаты. В обзор включены 28 исследований (18 по ПЭ, 10 по кровотечениям). Наиболее часто применялись градиентный бустинг, случайный лес, XGBoost и нейросетевые модели. Для ПЭ наилучшие результаты чаще демонстрировали модели, объединяющие материнские факторы риска с показателями артериального давления и компонентами скрининга I триместра (доплерометрия маточных артерий и плацентарные биомаркеры). Для прогнозирования кровотечений наиболее перспективными оказались подходы на базе электронных медицинских карт и предоперационных клинико-лабораторных данных, включая риск-стратификацию у пациенток с предлежанием плаценты (placenta previa) и врастанием плаценты (placenta accreta spectrum, PAS). Основным ограничением доказательной базы остаются преобладание ретроспективных дизайнов и дефицит внешней/проспективной валидации, что снижает переносимость моделей между клиниками и во времени.

Заключение

Заключение. Модели ИИ, обученные на мультипараметрических данных мониторинга, способны улучшать прогнозирование ПЭ и акушерских кровотечений, однако клиническое внедрение требует стандартизированной отчетности, внешней проверки и контроля калибровки моделей в реальной практике.

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