Obstet Gynecol Sci.  2022 Mar;65(2):113-124. 10.5468/ogs.21234.

Artificial intelligence in obstetrics

Affiliations
  • 1Department of Obstetrics and Gynecology, Korea University Anam Hospital, Seoul, Korea
  • 2AI Center, Korea University Anam Hospital, Seoul, Korea

Abstract

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

Keyword

Mother; Fetus; Disease; Diagnosis; Artificial intelligence

Reference

References

1. National Health Service (NHS). Annual report and accounts 2017/18. Leeds: NHS;2018.
2. Warrick PA, Hamilton EF, Precup D, Kearney RE. Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography. IEEE Trans Biomed Eng. 2010; 57:771–9.
Article
3. Aung MT, Yu Y, Ferguson KK, Cantonwine DE, Zeng L, McElrath TF, et al. Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers. Sci Rep. 2019; 9:17049.
Article
4. Burgos-Artizzu XP, Baños N, Coronado-Gutiérrez D, Ponce J, Valenzuela-Alcaraz B, Moreno-Espinosa AL, et al. Mid-trimester prediction of spontaneous preterm birth with automated cervical quantitative ultrasound texture analysis and cervical length: a prospective study. Sci Rep. 2021; 11:7469.
Article
5. Al-Rubaie ZTA, Hudson HM, Jenkins G, Mahmoud I, Ray JG, Askie LM, et al. Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: a model development and validation study. BMC Pregnancy Childbirth. 2020; 20:23.
Article
6. Antwi E, Amoakoh-Coleman M, Vieira DL, Madhavaram S, Koram KA, Grobbee DE, et al. Systematic review of prediction models for gestational hypertension and pre-eclampsia. PLoS One. 2020; 15:e0230955.
Article
7. Leite DFB, Cecatti JG. Fetal growth restriction prediction: how to move beyond. Scientific World Journal. 2019; 2019:1519048.
Article
8. Koivu A, Sairanen M. Predicting risk of stillbirth and pre-term pregnancies with machine learning. Health Inf Sci Syst. 2020; 8:14.
Article
9. Åmark H, Westgren M, Persson M. Prediction of stillbirth in women with overweight or obesity-a register-based cohort study. PLoS One. 2018; 13:e0206940.
Article
10. Yerlikaya G, Akolekar R, McPherson K, Syngelaki A, Nicolaides KH. Prediction of stillbirth from maternal demographic and pregnancy characteristics. Ultrasound Obstet Gynecol. 2016; 48:607–12.
Article
11. Berry MJA, Linoff GS. Data mining techniques. 2nd ed. Indianapolis (IN): Wiley;2004.
12. Han J, Micheline K. Data Mining: Concepts and Techniques. 2nd ed. San Francisco (CA): Elsevier;2006.
13. Han J, Micheline K, Pei J. Data Mining: Concepts and Techniques. 3rd ed. San Francisco (CA): Elsevier;2012.
14. Tan PN, Steinbach M, Karpatne A, Kumar V. Introduction to data mining. 2nd ed. London (UK): Pearson;2018.
15. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: a survey. Heliyon. 2018; 4:e00938.
Article
16. Song X, Mitnitski A, Cox J, Rockwood K. Comparison of machine learning techniques with classical statistical models in predicting health outcomes. Stud Health Technol Inform. 2004; 107(Pt 1):736–40.
17. Goodwin LK, Maher S. Data mining for preterm birth prediction. Como. 2000; 1:46–51.
Article
18. Goodwin LK, Iannacchione MA, Hammond WE, Crockett P, Maher S, Schlitz K. Data mining methods find demographic predictors of preterm birth. Nurs Res. 2001; 50:340–5.
Article
19. Goodwin LK, Iannacchione MA. Data mining methods for improving birth outcomes prediction. Outcomes Manag. 2002; 6:80–5.
20. Lee KS, Ahn KH. Artificial neural network analysis of spontaneous preterm labor and birth and its major determinants. J Korean Med Sci. 2019; 34:e128.
Article
21. 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; 35:e105.
Article
22. Parker MG, Ouyang F, Pearson C, Gillman MW, Belfort MB, Hong X, et al. Prepregnancy body mass index and risk of preterm birth: association heterogeneity by preterm subgroups. BMC Pregnancy Childbirth. 2014; 14:153.
Article
23. Heude B, Thiébaugeorges O, Goua V, Forhan A, Kaminski M, Foliguet B, et al. Pre-pregnancy body mass index and weight gain during pregnancy: relations with gestational diabetes and hypertension, and birth outcomes. Matern Child Health J. 2012; 16:355–63.
Article
24. Shin D, Song WO. Prepregnancy body mass index is an independent risk factor for gestational hypertension, gestational diabetes, preterm labor, and small- and large-for-gestational-age infants. J Matern Fetal Neonatal Med. 2015; 28:1679–86.
Article
25. Sibai BM, Caritis SN, Hauth JC, MacPherson C, VanDorsten JP, Klebanoff M, et al. Preterm delivery in women with pregestational diabetes mellitus or chronic hypertension relative to women with uncomplicated pregnancies. The National institute of Child health and Human Development Maternal-Fetal Medicine Units Network. Am J Obstet Gynecol. 2000; 183:1520–4.
26. Hedderson MM, Ferrara A, Sacks DA. Gestational diabetes mellitus and lesser degrees of pregnancy hyperglycemia: association with increased risk of spontaneous preterm birth. Obstet Gynecol. 2003; 102:850–6.
Article
27. Zhang J, Villar J, Sun W, Merialdi M, Abdel-Aleem H, Mathai M, et al. Blood pressure dynamics during pregnancy and spontaneous preterm birth. Am J Obstet Gynecol. 2007; 197:162e1–6.
Article
28. O’Hara S, Zelesco M, Sun Z. Cervical length for predicting preterm birth and a comparison of ultrasonic measurement techniques. Australas J Ultrasound Med. 2013; 16:124–34.
Article
29. Society for Maternal-Fetal Medicine (SMFM), McIntosh J, Feltovich H, Berghella V, Manuck T. The role of routine cervical length screening in selected high- and low-risk women for preterm birth prevention. Am J Obstet Gynecol. 2016; 215:B2–7.
Article
30. Berghella V, Pereira L, Gariepy A, Simonazzi G. Prior cone biopsy: prediction of preterm birth by cervical ultrasound. Am J Obstet Gynecol. 2004; 191:1393–7.
Article
31. Bevis KS, Biggio JR. Cervical conization and the risk of preterm delivery. Am J Obstet Gynecol. 2011; 205:19–27.
Article
32. Pinborg A, Ortoft G, Loft A, Rasmussen SC, Ingerslev HJ. Cervical conization doubles the risk of preterm and very preterm birth in assisted reproductive technology twin pregnancies. Hum Reprod. 2015; 30:197–204.
Article
33. Cho SH, Park KH, Jung EY, Joo JK, Jang JA, Yoo HN. Maternal characteristics, short mid-trimester cervical length, and preterm delivery. J Korean Med Sci. 2017; 32:488–94.
Article
34. Eke PI, Dye BA, Wei L, Slade GD, Thornton-Evans GO, Borgnakke WS, et al. Update on prevalence of periodontitis in adults in the united states: NHANES 2009 to 2012. J Periodontol. 2015; 86:611–22.
Article
35. Puertas A, Magan-Fernandez A, Blanc V, Revelles L, O’Valle F, Pozo E, et al. Association of periodontitis with preterm birth and low birth weight: a comprehensive review. J Matern Fetal Neonatal Med. 2018; 31:597–602.
Article
36. Vakil N, van Zanten SV, Kahrilas P, Dent J, Jones R; Global Consensus Group. The Montreal definition and classification of gastroesophageal reflux disease: a global evidence-based consensus. Am J Gastroenterol. 2006; 101:1900–20. quiz 1943.
Article
37. Patrick L. Gastroesophageal reflux disease (GERD): a review of conventional and alternative treatments. Altern Med Rev. 2011; 16:116–33.
38. Vinesh E, Masthan K, Kumar MS, Jeyapriya SM, Babu A, Thinakaran M. A clinicopathologic study of oral changes in gastroesophageal reflux disease, gastritis, and ulcerative colitis. J Contemp Dent Pract. 2016; 17:943–7.
Article
39. Deppe H, Mücke T, Wagenpfeil S, Kesting M, Rozej A, Bajbouj M, et al. Erosive esophageal reflux vs. non erosive esophageal reflux: oral findings in 71 patients. BMC Oral Health. 2015; 15:84.
Article
40. Ali RA, Egan LJ. Gastroesophageal reflux disease in pregnancy. Best Pract Res Clin Gastroenterol. 2007; 21:793–806.
Article
41. Koivu A, Sairanen M. Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Inf Sci Syst. 2020; 8:14.
Article
42. Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, Iram S. Prediction of preterm deliveries from EHG signals using machine learning. PLoS One. 2013; 8:e77154.
Article
43. Sadi-Ahmed N, Kacha B, Taleb H, Kedir-Talha M. Relevant features selection for automatic prediction of preterm deliveries from pregnancy ElectroHysterograhic (EHG) records. J Med Syst. 2017; 41:204.
Article
44. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Siem Reap. 2018; 1:1097–105.
45. Lee KS, Park KW. Social determinants of the association among cerebrovascular disease, hearing loss and cognitive impairment in a middle-aged or older population: recurrent neural network analysis of the Korean Longitudinal Study of Aging (2014–2016). Geriatr Gerontol Int. 2019; 19:711–6.
Article
46. Gao C, Osmundson S, Velez Edwards DR, Jackson GP, Malin BA, Chen Y. Deep learning predicts extreme pre-term birth from electronic health records. J Biomed Inform. 2019; 100:103334.
Article
47. Grigorescu I, Cordero-Grande L, Edwards AD, Hajnal J, Modat M, Deprez M. Interpretable convolutional neural networks for preterm birth classification [Internet]. arXiv.org. c2019. [cited 2021 Jun 15]. Available from: https://arxiv.org/abs/1910.00071 .
48. Naimi AI, Platt RW, Larkin JC. Machine learning for fetal growth prediction. Epidemiology. 2018; 29:290–8.
Article
49. Fung R, Villar J, Dashti A, Ismail LC, Staines-Urias E, Ohuma EO, et al. Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study. Lancet Digit Health. 2020; 2:e368–75.
50. Signorini MG, Pini N, Malovini A, Bellazzi R, Magenes G. Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring. Comput Methods Programs Biomed. 2020; 185:105015.
Article
51. Pini N, Lucchini M, Esposito G, Tagliaferri S, Campanile M, Magenes G, et al. A machine learning approach to monitor the emergence of late intrauterine growth restriction. Front Artif Intell. 2021; 4:622616.
Article
52. Lee KS, Kim HY, Lee SJ, Kwon SO, Na S, Hwang HS, et al. Prediction of newborn’s body mass index using nationwide multicenter ultrasound data: a machine-learning study. BMC Pregnancy Childbirth. 2021; 21:172.
Article
53. Sridar P, Kumar A, Quinton A, Nanan R, Kim J, Krishnakumar R. Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks. Ultrasound Med Biol. 2019; 45:1259–73.
Article
54. Venkatesh KK, Strauss RA, Grotegut CA, Heine RP, Chescheir NC, Stringer JSA, et al. Machine learning and statistical models to predict postpartum hemorrhage. Obstet Gynecol. 2020; 135:935–44.
Article
55. Asali A, Ravid D, Shalev H, David L, Yogev E, Yogev SS, et al. Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data. Arch Gynecol Obstet. 2021; 304:641–7.
Article
56. Betts KS, Kisely S, Alati R. Predicting common maternal postpartum complications: leveraging health administrative data and machine learning. BJOG. 2019; 126:702–9.
Article
57. Tsur A, Batsry L, Toussia-Cohen S, Rosenstein MG, Barak O, Brezinov Y, et al. Development and validation of a machine-learning model for prediction of shoulder dystocia. Ultrasound Obstet Gynecol. 2020; 56:588–96.
Article
58. Guedalia J, Sompolinsky Y, Novoselsky Persky M, Cohen SM, Kabiri D, Yagel S, et al. Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study. BJOG. 2021; 128:1824–32.
Article
59. Guedalia J, Lipschuetz M, Novoselsky-Persky M, Cohen SM, Rottenstreich A, Levin G, et al. Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries. Am J Obstet Gynecol. 2020; 223:437.e1–437.e15..
Article
60. Meyer R, Hendin N, Zamir M, Mor N, Levin G, Sivan E, et al. Implementation of machine learning models for the prediction of vaginal birth after cesarean delivery. J Matern Fetal Neonatal Med. 2020. Oct. 25. [Epub]. https://doi.org/10.1080/14767058.2020.1837769 .
Article
61. Liu LC, Tsai YH, Chou YC, Jheng YC, Lin CK, Lyu NY, et al. Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks. J Chin Med Assoc. 2021; 84:158–64.
Article
62. Zhong W, Liao L, Guo X, Wang G. A deep learning approach for fetal QRS complex detection. Physiol Meas. 2018; 39:045004.
Article
63. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol. 2020; 56:498–505.
Article
64. Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine learning in fetal cardiology: what to expect. Fetal Diagn Ther. 2020; 47:363–72.
Article
65. Miyagi Y, Hata T, Bouno S, Koyanagi A, Miyake T. Recognition of facial expression of fetuses by artificial intelligence (AI). J Perinat Med. 2021; 49:596–603.
Article
66. Beksaç MS, Durak B, Ozkan O, Cakar AN, Balci S, Karakaş U, et al. An artificial intelligent diagnostic system with neural networks to determine genetical disorders and fetal health by using maternal serum markers. Eur J Obstet Gynecol Reprod Biol. 1995; 59:131–6.
Article
67. Kojita Y, Matsuo H, Kanda T, Nishio M, Sofue K, Nogami M, et al. Deep learning model for predicting gestational age after the first trimester using fetal MRI. Eur Radiol. 2021; 31:3775–82.
Article
68. Torrents-Barrena J, Monill N, Piella G, Gratacós E, Eixarch E, Ceresa M, et al. Assessment of radiomics and deep learning for the segmentation of fetal and maternal anatomy in magnetic resonance imaging and ultrasound. Acad Radiol. 2021; 28:173–88.
Article
69. Looney P, Stevenson GN, Nicolaides KH, Plasencia W, Molloholli M, Natsis S, et al. Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. JCI insight. 2018; 3:e120178.
Article
70. Morris SA, Lopez KN. Deep learning for detecting congenital heart disease in the fetus. Nat Med. 2021; 27:764–5.
Article
71. Gnanadass I. Prediction of gestational diabetes by machine learning algorithms. IEEE Potentials. 2020; 39:32–7.
Article
72. Wu YT, Zhang CJ, Mol BW, Kawai A, Li C, Chen L, et al. Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning. J Clin Endocrinol Metab. 2021; 106:e1191–205.
Article
73. Hoffman MK, Ma N, Roberts A. A machine learning algorithm for predicting maternal readmission for hypertensive disorders of pregnancy. Am J Obstet Gynecol MFM. 2021; 3:100250.
Article
74. Yang L, Sun G, Wang A, Jiang H, Zhang S, Yang Y, et al. Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm. Technol Health Care. 2020; 28(S1):181–6.
Article
75. Akazawa M, Hashimoto K, Noda K, Yoshida K. The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study. Obstet Gynecol Sci. 2021; 64:266–73.
Article
Full Text Links
  • OGS
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr