Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality globally. Accurate and early prediction is crucial for timely intervention and improved outcomes. Machine learning (ML) has emerged as a promising approach for identifying individuals at high risk of developing preeclampsia by leveraging complex patterns within diverse datasets. However, translating promising ML research models into effective, reliable, and scalable clinical deployment presents significant challenges. This article reviews the current landscape of machine learning applications in preeclampsia prediction, focusing on identified deployment patterns and key predictive features. We synthesize findings from recent literature, discussing commonly employed ML algorithms, the types of data and features utilized (including maternal characteristics, biomarkers, and clinical history), and the reported predictive performance. Crucially, we examine the challenges and considerations related to the practical implementation of these models within healthcare systems, including data quality, model interpretability, integration into clinical workflows, and the necessity of robust MLOps practices. This review highlights the critical need to address deployment-related aspects to ensure that ML models for preeclampsia prediction can move beyond research settings and achieve real-world clinical impact, ultimately contributing to improved maternal health outcomes.

Preeclampsia,, Machine Learning, Prediction

Duley, L. The Global Impact of Pre-eclampsia and Eclampsia. Semin. Perinatol. 2009, 33, 130–137.

Magee, L.A.; Nicolaides, K.H.; von Dadelszen, P. Preeclampsia. N. Engl. J. Med. 2022, 386, 1817–1832.

Van Doorn, R.; Mukhtarova, N.; Flyke, I.P.; Lasarev, M.; Kim, K.; Hennekens, C.H.; Hoppe, K.K. Dose of aspirin to prevent preterm preeclampsia in women with moderate or high-risk factors: A systematic review and meta-analysis. PLoS ONE 2021, 16, e0247782.

O’Gorman, N.; Wright, D.; Syngelaki, A.; Akolekar, R.; Wright, A.; Poon, L.C.; Nicolaides, K.H. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11–13 weeks gestation. Am. J. Obstet. Gynecol. 2016, 214, e1–e103.

Hackelöer, M.; Schmidt, L.; Verlohren, S. New advances in prediction and surveillance of preeclampsia: Role of machine learning approaches and remote monitoring. Arch. Gynecol. Obstet. 2023, 308, 1663–1677.

Wright, D.; Gallo, D.M.; Gil Pugliese, S.; Casanova, C.; Nicolaides, K.H. Contingent screening for preterm pre-eclampsia. Ultrasound Obstet. Gynecol. 2016, 47, 554–559.

Ranjbar, A.; Montazeri, F.; Ghamsari, S.R.; Mehrnoush, V.; Roozbeh, N.; Darsareh, F. Machine learning models for predicting preeclampsia: A systematic review. BMC Pregnancy Childbirth 2024, 24, 6.

Brunelli, V.B.; Prefumo, F. Quality of first trimester risk prediction models for pre-eclampsia: A systematic review. BJOG 2015, 122, 904–914.

Bertini, A.; Salas, R.; Chabert, S.; Sobrevia, L.; Pardo, F. Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Front. Bioeng. Biotechnol. 2022, 9, 780389.

Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Syst. Rev. 2021, 10, 89.

Ao, C.; Zhou, W.; Gao, L.; Dong, B.; Yu, L. Prediction of antioxidant proteins using hybrid feature representation method and random forest. Genomics 2020, 112, 4666–4674.

Moons, K.G.; Wolff, R.F.; Riley, R.D.; Whiting, P.F.; Westwood, M.; Collins, G.S.; Reitsma, J.B.; Kleijnen, J.; Mallett, S. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Annals of Internal Medicine. Am. Coll. Physicians 2019, 170, W1–W33.

Velikova, M.; Lucas, P.J.F.; Spaanderman, M. A Predictive Bayesian Network Model for Home Management of Preeclampsia. In Artificial Intelligence in Medicine AIME 2011 Lecture Notes in Computer Science; Peleg, M., Lavrač, N., Combi, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; Volume 6747, pp. 179–183.

Martínez-Velasco, A.; Martínez-Villaseñor, L.; Miralles-Pechuán, L. Machine learning approach for pre-eclampsia risk factors association. In Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good, Bologna, Italy, 28–30 November 2018; Association for Computing Machinery: New York, NY, USA, 2018; pp. 232–237.

Kovacheva, V.P.; Eberhard, B.W.; Cohen, R.Y.; Maher, M.; Saxena, R.; Gray, K.J. Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores from Clinical and Genetic Risk Factors in Early and Late Pregnancies. Hypertension 2024, 81, 264–272.

Eberhard, B.W.; Cohen, R.Y.; Rigoni, J.; Bates, D.W.; Gray, K.J.; Kovacheva, V.P. An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning. medRxiv [Internet]. 2023 Aug 16. Available online: http://www.ncbi.nlm.nih.gov/pubmed/37645797 (accessed on 22 February 2024).

Li, Z.; Xu, Q.; Sun, G.; Jia, R.; Yang, L.; Liu, G.; Hao, D.; Zhang, S.; Yang, Y.; Li, X.; et al. Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm. Front. Physiol. 2022, 13, 1035726.

Liu, M.; Yang, X.; Chen, G.; Ding, Y.; Shi, M.; Sun, L.; Huang, Z.; Liu, J.; Liu, T.; Yan, R.; et al. Development of a prediction model on preeclampsia using machine learning-based method: A retrospective cohort study in China. Front. Physiol. 2022, 13, 896969.

Li, Y.X.; Shen, X.P.; Yang, C.; Cao, Z.Z.; Du, R.; Wang, J.P.; Wang, M. Novel electronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms. Pregnancy Hypertens. 2021, 26, 102–109.

Sufriyana, H.; Wu, Y.W.; Su, E.C.Y. Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia. EBioMedicine 2020, 54, 102710.

Neocleous, C.; Nicolaides, K.; Neokleous, K.; Schizas, C. Ethnicity as a Factor for the Estimation of the Risk for Preeclampsia: A Neural Network Approach. In Proceedings of the Artificial Intelligence: Theories, Models and Applications: 6th Hellenic Conference on AI, SETN 2010, Athens, Greece, 4–7 May 2010; LNAI; Springer: Berlin/Heidelberg, Germany, 2010; Volume 6040.

Bennett, R.; Mulla, Z.D.; Parikh, P.; Hauspurg, A.; Razzaghi, T. An imbalance-aware deep neural network for early prediction of preeclampsia. PLoS ONE 2022, 17, e0266042.

Neocleous, C.K.; Anastasopoulos, P.; Nikolaides, K.H.; Schizas, C.N.; Neokleous, K.C. Neural networks to estimate the risk for preeclampsia occurrence. In Proceedings of the IEEE International Joint Conference on Neural Networks, Atlanta, GA, USA, 14–19 June 2009; pp. 2221–2224.

Torres-Torres, J.; Villafan-Bernal, J.R.; Martinez-Portilla, R.J.; Hidalgo-Carrera, J.A.; Estrada-Gutierrez, G.; Adalid-Martinez-Cisneros, R.; Rojas-Zepeda, L.; Acevedo-Gallegos, S.; Camarena-Cabrera, D.M.; Cruz-Martínez, M.Y.; et al. Performance of a machine learning approach for the prediction of pre-eclampsia in a middle-income country. Ultrasound Obstet. Gynecol. 2024, 63, 350–357.

Gil, M.M.; Cuenca-Gómez, D.; Rolle, V.; Pertegal, M.; Díaz, C.; Revello, R.; Adiego, B.; Mendoza, M.; Molina, F.S.; Santacruz, B.; et al. Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study. Ultrasound Obstet. Gynecol. 2024, 63, 68–74.

Ansbacher-Feldman, Z.; Syngelaki, A.; Meiri, H.; Cirkin, R.; Nicolaides, K.H.; Louzoun, Y. Machine-learning-based prediction of pre-eclampsia using first-trimester maternal characteristics and biomarkers. Ultrasound Obstet. Gynecol. 2022, 60, 739–745.