Novel Technologies in Preterm Birth Prediction: Current Advances and Ethical Challenges

Kassenova, Marzhan A.; Ryskulova, Alma-Gul’ R.; Baimuratova, Mairash A.; Sokolova, Tatyana M.; Adyrbekova, Assel K.; Yesmakhanova, Indira S.

Novel Technologies in Preterm Birth Prediction: Current Advances and Ethical Challenges

Journal of Mother and Child

Volume 29 (2025): Issue 1 (February 2025)

By: Marzhan A. Kassenova

, Alma-Gul’ R. Ryskulova

, Mairash A. Baimuratova

, Tatyana M. Sokolova

, Assel K. Adyrbekova

and Indira S. Yesmakhanova

Open Access

|May 2025

References

Denney JM, Nelson E, Wadhwa P, Waters T, Mathew L, Goldenberg RL, et al. Cytokine profiling: variation in immune modulation with preterm birth vs. uncomplicated term birth identifies pivotal signals in pathogenesis of preterm birth. J Perinat Med. 2021 Mar 26;49(3):299–309. doi: 10.1515/jpm-2020-0025. Epub 2020 Oct 12.
Open DOI Search in Google Scholar Back to article
Kabyl BK, Isenova SS, Nurlanova GK, Buribayeva JK, Adilova KM, Ayazbay KM, et al. Predictors and risk factors of spontaneous premature birth: anamnestic characteristics, ultrasound and biomarkers (literature review). Reproductive Medicine. 2023;3(56):63–71. Doi: 10.37800/RM.3.2023.63-71. https://doi.org/10.37800/RM.3.2023.63-71.
Open DOI Search in Google Scholar Back to article
Conde-Agudelo A, Romero R. Vaginal progesterone for the prevention of preterm birth: who can benefit and who cannot? Evidence-based recommendations for clinical use. J Perinat Med. 2023 Jan 27;51(1):125–34. doi: 10.1515/jpm-2022-0462. Epub 2022 Dec 7.
Open DOI Search in Google Scholar Back to article
Jaiman S, Romero R, Bhatti G, Jung E, Gotsch F, Suksai M, et al. The role of the placenta in spontaneous preterm labor and delivery with intact membranes. J Perinat Med. 2022 Mar 4;50(5):553–66. doi: 10.1515/jpm-2021-0681.
Open DOI Search in Google Scholar Back to article
Hedderich DM, Boeckh-Behrens T, Bäuml JG, Menegaux A, Daamen M, Zimmer C, et al. Sequelae of premature birth in young adults: Incidental findings on routine brain MRI. Clin Neuroradiol. 2021 Jun;31(2):325–33. doi: 10.1007/s00062-020-00901-6. Epub 2020 Apr 14.
Open DOI Search in Google Scholar Back to article
Gao C, Osmundson S, Velez Edwards DR, Jackson GP, Malin BA, Chen Y. Deep learning predicts extreme preterm birth from electronic health records. J Biomed Inform. 2019 Dec;100:103334. doi: 10.1016/j.jbi.2019.103334. Epub 2019 Oct 31.
Open DOI Search in Google Scholar Back to article
Sun Y, Lian F, Deng Y, Liao S, Wang Y. Development and validation of a nomogram to predict spontaneous preterm birth in singleton gestation with short cervix and no history of spontaneous preterm birth. Heliyon. 2023 Sep 27;9(10):e20453. doi: 10.1016/j.heliyon.2023.e20453.
Open DOI Search in Google Scholar Back to article
Romero R, Conde-Agudelo A, Da Fonseca E, O’Brien JM, Cetingoz E, Creasy GW, et al. Vaginal progesterone for preventing preterm birth and adverse perinatal outcomes in singleton gestations with a short cervix: a meta-analysis of individual patient data. Am J Obstet Gynecol. 2018 Feb;218(2):161–80. doi: 10.1016/j.ajog.2017.11.576. Epub 2017 Nov 17.
Open DOI Search in Google Scholar Back to article
Jin Z, Chen L, Qiao D, Tiwari A, Jaunky CD, Sun B, et al. Cervical pessary for preventing preterm birth: a meta-analysis. J Matern Fetal Neonatal Med. 2019 Apr;32(7):1148–54. doi: 10.1080/14767058.2017.1401998. Epub 2017 Nov 20.
Open DOI Search in Google Scholar Back to article
Ferreira A, Bernardes J, Gonçalves H. Risk scoring systems for preterm birth and their performance: A systematic review. J Clin Med. 2023 Jun 28;12(13):4360. doi: 10.3390/jcm12134360.
Open DOI Search in Google Scholar Back to article
Brown R, Gagnon R, Delisle MF. No. 373-cervical insufficiency and cervical cerclage. J Obstet Gynaecol Can. 2019 Feb;41(2):233–47. doi: 10.1016/j.jogc.2018.08.009.
Open DOI Search in Google Scholar Back to article
Lee KS, Ahn KH. Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth. Diagnostics (Basel). 2020 Sep 22;10(9):733. doi: 10.3390/diagnostics10090733.
Open DOI Search in Google Scholar Back to article
Goodfellow L, Care A, Sharp A, Ivandic J, Poljak B, Roberts D, et al. Effect of QUiPP prediction algorithm on treatment decisions in women with a previous preterm birth: a prospective cohort study. BJOG. 2019 Dec;126(13):1569–75. doi: 10.1111/1471-0528.15886. Epub 2019 Aug 24.
Open DOI Search in Google Scholar Back to article
Koivu A, Sairanen M. Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Inf Sci Syst. 2020 Mar 25;8(1):14. doi: 10.1007/s13755-020-00105-9.
Open DOI Search in Google Scholar Back to article
Tekesin I. Pregnancy outcome in foetuses with increased nuchal translucency - 10-years’ experience in a prenatal medical practice. J Obstet Gynaecol. 2020 May;40(4):455–60. doi: 10.1080/01443615.2019.1621822. Epub 2019 Aug 16.
Open DOI Search in Google Scholar Back to article
Vovsha I, Salleb-Aouissi A, Raja A, Koch T, Rybchuk A, Radeva A, et al. Using kernel methods and model selection for prediction of preterm birth. In: Proceedings of the 1st Machine Learning for Healthcare Conference. PMLR Proceedings of Machine Learning Research. 2016;56 (1st Machine Learning for Healthcare Conference, 19–20 August 2016, Children’s Hospital LA, Los Angeles, CA, USA):55–72. Available at: https://proceedings.mlr.press/v56/Vovsha16.html.
Search in Google Scholar Back to article
Cobo T, Burgos-Artizzu XP, Ferrero S, Balcells J, Bosch J, Gené A, et al. External validation of a non-invasive vaginal tool to assess the risk of intra-amniotic inflammation in pregnant women with preterm labor and intact membranes. J Perinat Med. 2024 Nov 25;53(2):170–8. doi: 10.1515/jpm-2024-0178.
Open DOI Search in Google Scholar Back to article
Akazawa M, Hashimoto K. Prediction of preterm birth using artificial intelligence: a systematic review. J Obstet Gynaecol. 2022 Aug;42(6):1662–8. doi: 10.1080/01443615.2022.2056828. Epub 2022 Jun 1.
Open DOI Search in Google Scholar Back to article
Andrade Júnior VL, França MS, Santos RAF, Hatanaka AR, Cruz JJ, Hamamoto TEK, et al. A new model based on artificial intelligence to screening preterm birth. J Matern Fetal Neonatal Med. 2023 Dec;36(2):2241100. doi: 10.1080/14767058.2023.2241100.
Open DOI Search in Google Scholar Back to article
Malea A-G, Holban Ş, Meliţă N. Analysis and determination of risk factors leading to preterm birth using data mining techniques in r. Dev Appl Syst. 2010:86.
Search in Google Scholar Back to article
Ivshin AA, Boldina YuS, Gusev AV. The role of artificial intelligence in predicting preterm birth. Роль искусственного интеллекта в прогнозировании преждевременных родов [Russ. J. Hum. Reprod]. Problemy Reproduktsii. 2021;27(5):121–9. https://doi.org/10.17116/repro202127051121.
Search in Google Scholar Back to article
Chen HY, Chuang CH, Yang YJ, Wu TP. Exploring the risk factors of preterm birth using data mining. Expert Syst Appl. 2011;38:5384–7. DOI: 10.1016/j.eswa.2010.10.017.
Open DOI Search in Google Scholar Back to article
Lee KS, Song IS, Kim ES, Ahn KH. Determinants of spontaneous preterm labor and birth including gastroesophageal reflux disease and periodontitis. J Korean Med Sci. 2020 Apr 13;35(14):e105. doi: 10.3346/jkms.2020.35.e105.
Open DOI Search in Google Scholar Back to article
Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina (Kaunas). 2020 Sep 8;56(9):455. doi: 10.3390/medicina56090455.
Open DOI Search in Google Scholar Back to article
Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019 Mar 19;19(1):64. doi: 10.1186/s12874-019-0681-4.
Open DOI Search in Google Scholar Back to article
Bhunia S, O’Brien S, Ling Y, Huang Z, Wu P, Yang Y. New approaches suggest term and preterm human fetal membranes may have distinct biomechanical properties. Sci Rep. 2022 Mar 24;12(1):5109. doi: 10.1038/s41598-022-09005-2.
Open DOI Search in Google Scholar Back to article
Zhang Y, Du S, Hu T, Xu S, Lu H, Xu C, et al. Establishment of a model for predicting preterm birth based on the machine learning algorithm. BMC Pregnancy Childbirth. 2023 Nov 10;23(1):779. doi: 10.1186/s12884-023-06058-7.
Open DOI Search in Google Scholar Back to article
Boelig RC, Kripalu V, Chen SL, Cruz Y, Roman A, Berghella V. Utility of follow-up cervical length screening in low-risk women with a cervical length of 26 to 29 mm. Am J Obstet Gynecol. 2021 Aug;225(2):179.e1–179.e6. doi: 10.1016/j.ajog.2021.02.027. Epub 2021 Feb 27.
Open DOI Search in Google Scholar Back to article
Borboa-Olivares H, Rodríguez-Sibaja MJ, Espejel-Nuñez A, Flores-Pliego A, Mendoza-Ortega J, Camacho-Arroyo I, et al. A novel predictive machine learning model integrating cytokines in cervical-vaginal mucus increases the prediction rate for preterm birth. Int Jp Mol Sci. 2023 Sep 8;24(18):13851. doi: 10.3390/ijms241813851.
Open DOI Search in Google Scholar Back to article
Lanera C, Berchialla P, Sharma A, Minto C, Gregori D, Baldi I. Screening PubMed abstracts: is class imbalance always a challenge to machine learning? Syst Rev. 2019 Dec 6;8(1):317. doi: 10.1186/s13643-019-1245-8.
Open DOI Search in Google Scholar Back to article
Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019 Jul;8(7):2328–31. doi: 10.4103/jfmpc.jfmpc_440_19.
Open DOI Search in Google Scholar Back to article
Patberg ET, Wells M, Vahanian SA, Zavala J, Bhattacharya S, Richmond D, et al. Use of cervical elastography at 18 to 22 weeks’ gestation in the prediction of spontaneous preterm birth. Am J Obstet Gynecol. 2021 Nov;225(5):525.e1–525.e9. doi: 10.1016/j.ajog.2021.05.017. Epub 2021 May 27.
Open DOI Search in Google Scholar Back to article
Shields LB, Weymouth C, Bramer KL, Robinson S, McGee D, Richards L, et al. Risk assessment of preterm birth through identification and stratification of pregnancies using a real-time scoring algorithm. SAGE Open Med. 2021 Jan 12;9:2050312120986729. doi: 10.1177/2050312120986729.
Open DOI Search in Google Scholar Back to article
Son M, Miller ES. Predicting preterm birth: Cervical length and fetal fibronectin. Semin Perinatol. 2017 Dec;41(8):445–451. doi: 10.1053/j.semperi.2017.08.002. Epub 2017 Sep 19.
Open DOI Search in Google Scholar Back to article
Marvin G, Nakatumba-Nabende J, Hellen N, Alam MGR. Responsible artificial intelligence for preterm birth prediction in vulnerable populations. 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2022. 2022:1–6. doi: 10.1109/CSDE56538.2022.10089301.
Open DOI Search in Google Scholar Back to article
Chakoory O, Barra V, Rochette E, Blanchon L, Sapin V, Merlin E, et al. DeepMPTB: a vaginal microbiome-based deep neural network as artificial intelligence strategy for efficient preterm birth prediction. Biomark Res. 2024 Feb 14;12(1):25. doi: 10.1186/s40364-024-00557-1.
Open DOI Search in Google Scholar Back to article
Włodarczyk T, Płotka S, Szczepański T, Rokita P, Sochacki-Wójcicka N, Wójcicki J, et al. Machine learning methods for preterm birth prediction: A review. Electronics. 2021 March 3;10(5):586. doi: 10.3390/electronics10050586.
Open DOI Search in Google Scholar Back to article
Raja R, Mukherjee I, Sarkar BK. A machine learning-based prediction model for preterm birth in rural India. J Healthc Eng. 2021 Jun 15;2021:6665573. doi: 10.1155/2021/6665573.
Open DOI Search in Google Scholar Back to article
Sharifi-Heris Z, Laitala J, Airola A, Rahmani AM, Bender M. Machine learning approach for preterm birth prediction using health records: Systematic review. JMIR Med Inform. 2022 Apr 20;10(4):e33875. doi: 10.2196/33875.
Open DOI Search in Google Scholar Back to article
Valavani E, Blesa M, Galdi P, Sullivan G, Dean B, Cruickshank H, et al. Language function following preterm birth: prediction using machine learning. Pediatr Res. 2022 Aug;92(2):480–9. doi: 10.1038/s41390-021-01779-x. Epub 2021 Oct 11.
Open DOI Search in Google Scholar Back to article

DOI: https://doi.org/10.34763/jmotherandchild.20252901.d-24-00048 | Journal eISSN: 2719-535X | Journal ISSN: 2719-6488

Journal RSS Feed

Language: English

Page range: 30 - 38

Submitted on: Nov 28, 2024

Accepted on: Mar 21, 2025

Published on: May 24, 2025

Published by: Institute of Mother and Child

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Prediction,

Preterm birth,

Risk factors of preterm birth

Related subjects:

Medicine,

Clinical medicine,

Pediatrics and juvenile medicine,

Paediatric haematology and oncology,

Public health

© 2025 Marzhan A. Kassenova, Alma-Gul’ R. Ryskulova, Mairash A. Baimuratova, Tatyana M. Sokolova, Assel K. Adyrbekova, Indira S. Yesmakhanova, published by Institute of Mother and Child
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous article Volume 29 (2025): Issue 1 (February 2025)Next article