Bokaie, Mahshid, Ijabi, Roghayeh (2026). Integration of Artificial Intelligence, Multi-Omics, and Digital Health for Precision Prevention of Preterm Birth: A Structured Narrative Review. Shahid Sadoughi University of Medical Sciences- Journal Management System, 8(2), 74-81. doi: 10.18502/wjpn.v8i2.22103
Mahshid Bokaie; Roghayeh Ijabi. "Integration of Artificial Intelligence, Multi-Omics, and Digital Health for Precision Prevention of Preterm Birth: A Structured Narrative Review". Shahid Sadoughi University of Medical Sciences- Journal Management System, 8, 2, 2026, 74-81. doi: 10.18502/wjpn.v8i2.22103
Bokaie, Mahshid, Ijabi, Roghayeh (2026). 'Integration of Artificial Intelligence, Multi-Omics, and Digital Health for Precision Prevention of Preterm Birth: A Structured Narrative Review', Shahid Sadoughi University of Medical Sciences- Journal Management System, 8(2), pp. 74-81. doi: 10.18502/wjpn.v8i2.22103
Bokaie, Mahshid, Ijabi, Roghayeh Integration of Artificial Intelligence, Multi-Omics, and Digital Health for Precision Prevention of Preterm Birth: A Structured Narrative Review. Shahid Sadoughi University of Medical Sciences- Journal Management System, 2026; 8(2): 74-81. doi: 10.18502/wjpn.v8i2.22103

Integration of Artificial Intelligence, Multi-Omics, and Digital Health for Precision Prevention of Preterm Birth: A Structured Narrative Review

Journal of Integrated Maternal and Pediatric Care
Volume 8, Issue 2, June 2026, Pages 74-81    PDF (399.2 K)
Document Type: Scientific Review
DOI: 10.18502/wjpn.v8i2.22103
Authors
Mahshid Bokaie1, 2; Roghayeh Ijabi* 3
1Research Center for Nursing and Midwifery Care, Comprehensive Research Institute for Maternal and Child Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2School of Nursing and Midwifery, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
3Student research committee, School of Nursing and Midwifery, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Abstract
Background: Preterm birth (PTB) remains a leading cause of neonatal morbidity and mortality worldwide. Conventional prediction methods have limited accuracy, highlighting the need for precision prevention strategies that integrate molecular, clinical, and digital health data.
Methods: A structured narrative review was conducted using PubMed, Scopus, and Web of Science to identify relevant studies published between 2010 and 2024. A total of 21 relevant studies were included and narratively synthesized into three thematic areas: multi-omics technologies, artificial intelligence (AI)-based prediction models, and digital health approaches for PTB prevention.
Results: Multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling, improved understanding of PTB pathophysiology and identified biomarkers associated with inflammation, immune dysregulation, oxidative stress, extracellular matrix remodeling, and placental dysfunction. AI models integrating clinical, imaging, laboratory, and molecular data demonstrated promising predictive performance, with reported area under the receiver operating characteristic curve (AUC) values ranging from approximately 0.61 to 0.94 where available. Ensemble machine learning and deep learning algorithms generally outperformed conventional statistical approaches. Digital health technologies, including wearable devices and remote monitoring systems, supported continuous maternal assessment and facilitated earlier identification of women at increased risk. However, limited external validation, heterogeneous datasets, model interpretability, and data privacy remain important challenges to clinical implementation. Conclusion: The integration of multi-omics technologies, AI, and digital health represents a promising strategy for precision prevention of PTB. Future research should prioritize multicenter validation, standardized data integration, and the development of transparent and clinically applicable AI models to facilitate translation into routine obstetric care.
Keywords
Preterm birth; Precision prevention; Artificial intelligence; Machine learning; Multi-omics; Digital health
Full Text

1. van Zijl MD, Koullali B, Mol BW, Pajkrt E, Oudijk MA. Prevention of preterm delivery: current challenges and future prospects. Int J Womens Health 2016; 8: 633-45.
2. Samuel TM, Sakwinska O, Makinen K, Burdge GC, Godfrey KM, Silva-Zolezzi I. Preterm Birth: A Narrative Review of the Current Evidence on Nutritional and Bioactive Solutions for Risk Reduction. Nutrients 2019; 11(8): 1811.
3. Creswell L, Rolnik DL, Lindow SW, O'Gorman N. Preterm Birth: Screening and Prediction. Int J Womens Health 2023; 15: 1981-97.
4. Akazawa M, Hashimoto K. Prediction of preterm birth using artificial intelligence: a systematic review. J Obstet Gynaecol 2022; 42(6): 1662-8.
5. Lee Y. Artificial intelligence in preterm birth prediction: a narrative review of current approaches and clinical applicability. Obstet Gynecol Sci 2026; 69(2): 94-102.
6. Bradley E, Blencowe H, Moller AB, Okwaraji YB, Sadler F, Gruending A, et al. Born too soon: global epidemiology of preterm birth and drivers for change. Reprod Health 2025; 22(Suppl 2): 105.
7. Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, et al. Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technology and Health Care 2024; 32(3): 1273-87.
8. Seong D, Espinosa C, Aghaeepour N. Computational Approaches for Predicting Preterm Birth and Newborn Outcomes. Clin Perinatol 2024; 51(2): 461-73.                                                                                                                                           9. Wu Y. Machine Learning-Based Risk Prediction Models for Pregnancy-Related Syndromes. Birth Defects Res 2026; 118(3): e70038.
10. Zuo R, Zhang F, Shi C, Wang L, Wang Y, Tong L. Deep Learning in Predicting Preterm Birth: A Comparative Study of Machine Learning Algorithms. Matern Fetal Med 2024; 6(3): 141-6.
11. Cheng D, Li N, Sun Q, Wang K, Gao F. Vaginal microbiome and preterm birth: Composition, mechanisms and microbiota‑directed therapies (Review). Int J Mol Med 2025;56(6): 203.
12. Couture C, Brien ME, Boufaied I, Duval C, Soglio DD, Enninga EAL, et al. Proinflammatory changes in the maternal circulation, maternal-fetal interface, and placental transcriptome in preterm birth. Am J Obstet Gynecol 2023; 228(3): 332.e1-.e17.
13. Gupta JK, Alfirevic A. Systematic review of preterm birth multi-omic biomarker studies. Expert Rev Mol Med 2022; 24:1-24.
14. Alikamali M, Mohammad-Alizadeh-Charandabi S, Ahmadi S, Memar MY, Shahnazi M. The role of the vaginal microbiome in preterm premature rupture of membranes: a comprehensive review of mechanisms and clinical implications. Health Sci Rep 2025; 8(12): e71484.
15. Powell AM, Khan FZA, Ravel J, Elovitz MA. Untangling Associations of Microbiomes of Pregnancy and Preterm Birth. Clin Perinatol. 2024; 51(2): 425-39.
16. Teng X, Liu M, Wang Z, Dong X. Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study. Sci Rep 2025; 15(1): 10213.
17. Deuster E, Khalil A. Artificial Intelligence in Obstetrics: Current Applications, Opportunities, and Clinical Implementation Challenges. Clin Obstet Gynecol 2026; 69(1): 54-61.
18. Kang L, Luo D, Xie W, Luo X, Mei J, He J. An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development and external validation. Front Endocrinol (Lausanne) 2025: 16: 1665935.
19. Correia V, Mascarenhas T, Mascarenhas M. Smart Pregnancy: AI-Driven Approaches to Personalised Maternal and Foetal Health-A Scoping Review. J Clin Med 2025; 14(19): 6974.
20. Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine 2023; 158: 106848.
21. Liu L, Zhu Q, Zong Y, Chen X, Zhang W, Wang J. Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis. J Med Internet Res 2026; 28: e78714.

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