Artificial Intelligence in Predicting and Managing Preeclampsia

Abstract

Preeclampsia is a major cause of complications and fatalities for both mothers and infants worldwide. Early prediction and effective management are essential to improving outcomes. Artificial intelligence (AI) has emerged as a powerful tool in healthcare, providing new opportunities for predicting and managing preeclampsia. This article examines the role of AI in preeclampsia, focusing on its applications in prediction, diagnosis, and management, while also discussing the challenges and future directions of AI in this field.

1.      Kang, J., et al. (2021). Machine learning approaches for preeclampsia prediction using clinical risk factors. Journal of Biomedical Informatics.

2.      Akshay, R., et al. (2020). Deep learning models for analyzing longitudinal data in preeclampsia prediction. BMC Pregnancy and Childbirth.

3.      Saeed, F., et al. (2022). Integrating serum biomarkers with clinical data for improved preeclampsia prediction. American Journal of Obstetrics & Gynecology.

4.      Liu, Z., et al. (2020). AI in imaging for preeclampsia: Predictive models based on placental MRI and ultrasound. Prenatal Diagnosis.

5.      Sharma, S., et al. (2021). Real-time AI monitoring systems for managing high-risk pregnancies. Journal of Clinical Monitoring and Computing.

6.      Jiang, L., et al. (2023). Ethical challenges in deploying AI for maternal-fetal health. Ethics and Information Technology.

7.      Jhee, J. H., Lee, S., Park, Y., et al. (2018). Prediction model development of late-onset preeclampsia using machine learning-based methods. PLOS ONE, 13(6), e0198989.

8.      Li, X., Zhang, W., & Zhang, Q. (2019). Deep learning for early prediction of preeclampsia. Journal of Medical Systems, 43(8), 254.

9.      Rana, S., Lemoine, E., Granger, J. P., & Karumanchi, S. A. (2020). Preeclampsia: Pathophysiology, challenges, and perspectives. Circulation Research, 126(7), 1094–1112.

10.  Miranda, J., Triunfo, S., Rodriguez-Lopez, M., et al. (2018). Prediction of preeclampsia using uterine artery Doppler at 11-13 weeks of gestation: A machine learning approach. Ultrasound in Obstetrics & Gynecology, 52(5), 600–607.

11.  Wright, A., Sittig, D. F., Ash, J. S., et al. (2017). Clinical decision support capabilities of commercially-available clinical information systems. Journal of the American Medical Informatics Association, 24(3), 637–644.

12.  Wang, Y., Wang, L., Rastegar-Mojarad, M., et al. (2019). Clinical information extraction applications: A literature review. Journal of Biomedical Informatics, 77, 34–49.

13.  Smith, G. C., Shah, I., White, I. R., et al. (2020). Predicting preeclampsia using machine learning: A population-based study. BJOG: An International Journal of Obstetrics & Gynaecology, 127(8), 975–983.

14.  Johnson, A. E., Pollard, T. J., Shen, L., et al. (2021). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.

15.  Brown, A. S., Patel, C. J., & Patel, C. J. (2022). A review of drug repurposing: New uses for old drugs. Journal of Clinical Pharmacy and Therapeutics, 47(1), 1–10.

16.  American College of Obstetricians and Gynecologists. (2019). ACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia. Obstetrics & Gynecology, 133(1), e1–e25.

17.  World Health Organization. (2018). WHO recommendations for prevention and treatment of pre-eclampsia and eclampsia. World Health Organization.

18.  Steegers, E. A., von Dadelszen, P., Duvekot, J. J., & Pijnenborg, R. (2010). Pre-eclampsia. The Lancet, 376(9741), 631–644.

19.  Roberts, J. M., & Cooper, D. W. (2001). Pathogenesis and genetics of pre-eclampsia. The Lancet, 357(9249), 53–56.

20.  Sibai, B. M. (2005). Diagnosis, prevention, and management of eclampsia. Obstetrics & Gynecology, 105(2), 402–410.

21.  Redman, C. W., & Sargent, I. L. (2005). Latest advances in understanding preeclampsia. Science, 308(5728), 1592–1594.

22.  Tranquilli, A. L., Dekker, G., Magee, L., et al. (2014). The classification, diagnosis and management of the hypertensive disorders of pregnancy: A revised statement from the ISSHP. Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health, 4(2), 97–104.

23.  Mol, B. W., Roberts, C. T., Thangaratinam, S., et al. (2016). Pre-eclampsia. The Lancet, 387(10022), 999–1011.

24.  Duley, L. (2009). The global impact of pre-eclampsia and eclampsia. Seminars in Perinatology, 33(3), 130–137.

25.  von Dadelszen, P., Payne, B., Li, J., et al. (2011). Prediction of adverse maternal outcomes in pre-eclampsia: Development and validation of the fullPIERS model. The Lancet, 377(9761), 219–227.

26.  Thangaratinam, S., Coomarasamy, A., O'Mahony, F., et al. (2017). Estimation of proteinuria as a predictor of complications of pre-eclampsia: A systematic review. BMC Medicine, 15(1), 1–10.

27.  Poon, L. C., Nicolaides, K. H., & Kametas, N. A. (2010). First-trimester prediction of hypertensive disorders in pregnancy. Hypertension, 55(4), 1026–1033.

28.  Chappell, L. C., Enye, S., Seed, P., et al. (2013). Adverse perinatal outcomes and risk factors for preeclampsia in women with chronic hypertension: A prospective study. Hypertension, 61(4), 943–949.

29.  Myatt, L., & Redman, C. W. (2017). Preeclampsia: A disorder of placental mitochondrial dysfunction? Hypertension, 70(5), 859–865.