AI in the Delivery Room: Shaping the Future of Childbirth
DOI:
https://doi.org/10.12775/JEHS.2025.86.66466Keywords
artificial intelligence, diagnostic possibilities, IVF, obstetrics, treatmentAbstract
Background. Artificial intelligence (AI) is defined as the application of advanced algorithms and machine learning techniques to analyze large amounts of data. Its use in medicine concerns areas such as medical images, laboratory test results, and patient medical histories. Thanks to its predictive capabilities, AI can also forecast the risk of diseases, identify patterns in data, and discover new relationships, which can lead to better healthcare, faster diagnoses, and more effective therapies.
Aim. This study reviews current applications of artificial intelligence in obstetrics, highlighting its benefits in routine tests like ultrasound and its impact on IVF procedures.
Material and methods. The studies cited in the presented review were selected from PUBMED.The oldest article is from 2017, while the most citations come from articles from 2023. The key words used for the search included: ‘artificial intelligence’ and ‘obstetrics’. Articles not written in English were excluded.
Results. In obstetrics, artificial intelligence has applications in many examinations used on a daily basis, such as ultrasound or cardiotocography. In addition, it is also used, among other things, to analyse fetal heart echocardiography films and calculate the deviation from normal. Other uses of artificial intelligence can be seen in imaging methods such as MRI. The impact of this technology in the in vitro procedure should be noted as well.
Conclusions. AI technology will possibly bring opportunities for better medical care in obstetrics. It will enable better diagnosis and more effective treatment. It also brings an opportunity for the development of better treatments for infertility in women.
References
1. Emin EI, Emin E, Papalois A, Willmott F, Clarke S, Sideris M. Artificial intelligence in obstetrics and gynaecology: Is this the way forward? In Vivo. 2019; 33(5):1547-1551. https://doi.org/10.21873/invivo.11635
2. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol. 2020; 56(4):498-505.https://doi.org/10.1002/uog.22122
3. Yazdani A, Costa S, Kroon B. Artificial intelligence: Friend or foe? Aust N Z J Obstet Gynaecol. 2023; 63(2):127-130.
4. Ahn KH, Lee KS. Artificial intelligence in obstetrics. Obstet Gynecol Sci. 2022; 65(2):113-124. https://doi.org/10.5468/ogs.21234
5. Weichert J, Welp A, Scharf JL, Dracopoulos C, Becker WH, Gembicki M. The use of artificial intelligence in automation in the fields of gynaecology and obstetrics - an assessment of the state of play. Geburtshilfe Frauenheilkd. 2021; 81(11):1203-1216. https://doi.org/10.1055/a-1522-3029
6. Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril. 2020; 114(5):914-920. https://doi.org/10.1016/j.fertnstert.2020.09.157
7. Iftikhar P, Kuijpers MV, Khayyat A, Iftikhar A, DeGouvia De Sa M. Artificial intelligence: A new paradigm in obstetrics and gynecology research and clinical practice. Cureus. 2020; 12(2):e7124. https://doi.org/10.7759/cureus.7124
8. Kim HY, Cho GJ, Kwon HS. Applications of artificial intelligence in obstetrics. Ultrasonography. 2023; 42(1):2-9. https://doi.org/10.14366/usg.22063
9. Malani SN 4th, Shrivastava D, Raka MS. A comprehensive review of the role of artificial intelligence in obstetrics and gynecology. Cureus. 2023; 15(2):e34891. https://doi.org/10.7759/cureus.34891
10. Delanerolle G, Yang X, Shetty S, et al. Artificial intelligence: A rapid case for advancement in the personalization of gynaecology/obstetric and mental health care. Womens Health (Lond). 2021; 17:17455065211018111. https://doi.org/10.1177/17455065211018111
11. Madej M, Pogorzelska M, Wróblewski K, et al. Aspekty etyczne i perspektywy stosowania sztucznej inteligencji w medycynie. In: Krajewska-Kułak E, editor. W drodze do brzegu życia. Vol 17, 2019:123-138.
12. Arain Z, Iliodromiti S, Slabaugh G, David AL, Chowdhury TT. Machine learning and disease prediction in obstetrics. Curr Res Physiol. 2023; 6:100099. https://doi.org/10.1016/j.crphys.2023.100099
13. Zhao Z, Zhu J, Jiao P, et al. Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis. BMC Med Inform Decis Mak. 2024; 24(1):19. https://doi.org/10.1186/s12911-024-02423-4
14. Petrozziello A, Redman CWG, Papageorghiou AT, Jordanov I, Georgieva A. Multimodal convolutional neural networks to detect fetal compromise during labor and delivery. IEEE Access. 2019; 7:112026-112036. https://doi.org/10.1109/ACCESS.2019.2933368
15. Fergus P, Hussain A, Al-Jumeily D, Huang DS, Bouguila N. Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms. Biomed Eng Online. 2017; 16(1):89. https://doi.org/10.1186/s12938-017-0378-z
16. Fergus P, Chalmers C, Montañez CC, Reilly D, Lisboa P, Pineles B. Modelling segmented cardiotocography time-series signals using one-dimensional convolutional neural networks for the early detection of abnormal birth outcomes. IEEE Trans Emerg Top Comput Intell. 2020:1-11. https://doi.org/10.48550/arXiv.1908.02338
17. Zhou Z, Zhao Z, Zhang XX, Zhang X, Jiao P, Ye X. Identifying fetal status with fetal heart rate: Deep learning approach based on long convolutional networks. Comput Biol Med. 2023; 159:106970. https://doi.org/10.1016/j.compbiomed.2023.106970
18. Das S, Obaidullah SM, Mahmud M, et al. A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set.Sci Rep. 2023; 13(1):2495. https://doi.org/10.1038/s41598-023-27707-z
19. Nurmaini S, Rachmatullah MN, Sapitri AI, et al. Deep learning-based computer-aided fetal echocardiography: Application to heart standard view segmentation for congenital heart defects detection.Sensors (Basel). 2021; 21(23):8007. https://doi.org/10.3390/s21238007
20. Day TG, Kainz B, Hajnal J, Razavi R, Simpson JM. Artificial intelligence,fetal echocardiography,and congenital heart disease.Prenat Diagn. 2021; 41(6):733-742. https://doi.org/10.1002/pd.5892
21. Chen Z, Liu Z, Du M, Wang Z. Artificial intelligence in obstetric ultrasound: An update and future applications. Front Med (Lausanne). 2021; 8:733468. https://doi.org/10.3389/fmed.2021.733468
22. Wu H, Wu B, Lai F, et al. Application of artificial intelligence in anatomical structure recognition of standard section of fetal heart. Comput Math Methods Med. 2023; 2023:5650378. https://doi.org/10.1155/2023/5650378
23. Gong Y, Zhang Y, Zhu H, et al. Fetal congenital heart disease echocardiogram screening based on DGACNN: Adversarial one-class classification combined with video transfer learning. IEEE Trans Med Imaging. 2020; 39(4):1206-1222. https://doi.org/10.1109/TMI.2019.2946059
24. Sakai A, Komatsu M, Komatsu R, et al. Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening. Biomedicines. 2022; 10(3):551. https://doi.org/10.3390/biomedicines10030551
25. Herling L, Johnson J, Ferm-Widlund K, Zamprakou A, Westgren M, Acharya G. Automated quantitative evaluation of fetal atrioventricular annular plane systolic excursion. Ultrasound Obstet Gynecol. 2021; 58(6):853-863. https://doi.org/10.1002/uog.23703
26. Scharf JL, Dracopoulos C, Gembicki M, Welp A, Weichert J. How automated techniques ease functional assessment of the fetal heart: Applicability of MPI+™ for direct quantification of the modified myocardial performance index. Diagnostics (Basel). 2023; 13(10):1705. https://doi.org/10.3390/diagnostics13101705
27. Xiao S, Zhang J, Zhu Y, et al. Application and progress of artificial intelligence in fetal ultrasound. J Clin Med. 2023; 12(9):3298. https://doi.org/10.3390/jcm12093298
28. Gupta K, Balyan K, Lamba B, Puri M, Sengupta D, Kumar M. Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy. J Matern Fetal Neonatal Med. 2022; 35(25):5587-5594. https://doi.org/10.1080/14767058.2021.1887847
29. Burgos-Artizzu XP, Coronado-Gutiérrez D, Valenzuela-Alcaraz B, et al. Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age. Am J Obstet Gynecol MFM. 2021; 3(6):100462. https://doi.org/10.1016/j.ajogmf.2021.100462
30. Horgan R, Nehme L, Abuhamad A. Artificial intelligence in obstetric ultrasound: A scoping review. Prenat Diagn. 2023; 43(9):1176-1219. https://doi.org/10.1002/pd.6411
31. He F, Wang Y, Xiu Y, Zhang Y, Chen L. Artificial intelligence in prenatal ultrasound diagnosis. Front Med (Lausanne). 2021; 8:729978. https://doi.org/10.3389/fmed.2021.729978
32. Drukker L. Real-time identification of fetal anomalies on ultrasound using artificial intelligence: What's next? Ultrasound Obstet Gynecol. 2022; 59(3):285-287. https://doi.org/10.1002/uog.24869
33. Ramirez Zegarra R, Ghi T. Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound Obstet Gynecol. 2023; 62(2):185-194. https://doi.org/10.1002/uog.26130
34. Lin M, He X, Guo H, et al. Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations. Ultrasound Obstet Gynecol. 2022; 59(3):304-316. https://doi.org/10.1002/uog.24843
35. Stirnemann JJ, Besson R, Spaggiari E, et al. Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound Obstet Gynecol. 2023; 62(3):353-360. https://doi.org/10.1002/uog.26242
36. Vahedifard F, Adepoju JO, Supanich M, et al. Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging. World J Clin Cases. 2023; 11(16):3725-3735. https://doi.org/10.12998/wjcc.v11.i16.3725
37. Ciceri T, Squarcina L, Giubergia A, Bertoldo A, Brambilla P, Peruzzo D. Review on deep learning fetal brain segmentation from magnetic resonance images. Artif Intell Med. 2023; 143:102608. https://doi.org/10.1016/j.artmed.2023.102608
38. Meshaka R, Gaunt T, Shelmerdine SC. Artificial intelligence applied to fetal MRI: A scoping review of current research.Br J Radiol. 2023; 96(1147):20211205. https://doi.org/10.1259/bjr.20211205
39. Mennickent D, Rodríguez A, Opazo MC, et al.Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Front Endocrinol (Lausanne) 2023; 14:1130139. https://doi.org/10.3389/fendo.2023.1130139
40. Hajirasouliha I, Elemento O. Precision medicine and artificial intelligence: overview and relevance to reproductive medicine.Fertil Steril. 2020; 114(5):908-913. https://doi.org/10.1016/j.fertnstert.2020.09.156
41. Curchoe CL, Malmsten J, Bormann C, et al.Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?.Fertil Steril. 2020; 114(5):934-940. https://doi.org/10.1016/j.fertnstert.2020.10.040
42. Medenica S, Zivanovic D, Batkoska L, et al.The future is coming: Artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes—the value of regulatory frameworks.Diagnostics (Basel) 2022; 12(12):2979. https://doi.org/10.3390/diagnostics12122979
43. Rosenwaks Z. Artificial intelligence in reproductive medicine: a fleeting concept or the wave of the future?.Fertil Steril. 2020; 114(5):905-907. https://doi.org/10.1016/j.fertnstert.2020.10.002
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anna Oleszczuk, Weronika Woźniak, Patrycja Pelczar, Aleksandra Skowron, Żaneta Kimber-Trojnar, Bożena Leszczyńska-Gorzelak

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
Stats
Number of views and downloads: 33
Number of citations: 0