본문 바로가기
KERD
논문KCI 등재

Artificial intelligence in obstetrics

2

조회

0

다운로드

3

인용

0

참고문헌

키워드

주제분류산부인과학

초록

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

Abstract

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

참고문헌 (75)

  1. Annual report and accounts 2017, "18", National Health Service(NHS), 2018
  2. Warrick PA, "Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography", IEEE Trans Biomed Eng, 57, pp.771~779 (2010)
  3. Aung MT, "Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers", Sci Rep, 9, pp.17049~ (2019)
  4. Burgos-Artizzu XP, "Mid-trimester prediction of spontaneous preterm birth with automated cervical quantitative ultrasound texture analysis and cervical length : a prospective study", Sci Rep, 11, pp.7469~ (2021)
  5. Al-Rubaie ZTA, "Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics : a model development and validation study", BMC Pregnancy Childbirth, 20, pp.23~ (2020)
  6. Antwi E, "Systematic review of prediction models for gestational hypertension and preeclampsia", PLoS One, 15, pp.e0230955~ (2020)
  7. Leite DFB, "Fetal growth restriction prediction : how to move beyond", Scientific World Journal, 2019, pp.1519048~ (2019)
  8. Koivu A, "Predicting risk of stillbirth and preterm pregnancies with machine learning", Health Inf Sci Syst, 8, pp.14~ (2020)
  9. Åmark H, "Prediction of stillbirth in women with overweight or obesity-a register-based cohort study", PLoS One, 13, pp.e0206940~ (2018)
  10. Yerlikaya G, "Prediction of stillbirth from maternal demographic and pregnancy characteristics", Ultrasound Obstet Gynecol, 48, pp.607~612 (2016)
  11. Berry MJA, "Data mining techniques" (2004), Wiley
  12. Han J, "Data Mining: Concepts and Techniques" (2006), Elsevier
  13. Han J, "Data Mining: Concepts and Techniques" (2012), Elsevier
  14. Tan PN, "Introduction to data mining" (2018), Pearson
  15. Abiodun OI, "State-of-the-art in artificial neural network applications : a survey", Heliyon, 4, pp.e00938~ (2018)
  16. Song X, "Comparison of machine learning techniques with classical statistical models in predicting health outcomes", Stud Health Technol Inform, 107(Pt 1), pp.736~740 (2004)
  17. Goodwin LK, "Data mining for preterm birth prediction", Como, 1, pp.46~51 (2000)
  18. Goodwin LK, "Data mining methods find demographic predictors of preterm birth", Nurs Res, 50, pp.340~345 (2001)
  19. Goodwin LK, "Data mining methods for improving birth outcomes prediction", Outcomes Manag, 6, pp.80~85 (2002)
  20. 이광식, "Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants", Journal of Korean Medical Science, 34(16), pp.1~10 (2019), 대한의학회
  21. Lee KS, "Determinants of spontaneous preterm labor and birth including gastroesophageal reflux disease and periodontitis", J Korean Med Sci, 35, pp.e105~ (2020)
  22. Parker MG, "Prepregnancy body mass index and risk of preterm birth : association heterogeneity by preterm subgroups", BMC Pregnancy Childbirth, 14, pp.153~ (2014)
  23. Heude B, "Pre-pregnancy body mass index and weight gain during pregnancy : relations with gestational diabetes and hypertension, and birth outcomes", Matern Child Health J, 16, pp.355~363 (2012)
  24. Shin D, "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, 28, pp.1679~1686 (2015)
  25. Sibai BM, "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, 183, pp.1520~1524 (2000)
  26. Hedderson MM, "Gestational diabetes mellitus and lesser degrees of pregnancy hyperglycemia : association with increased risk of spontaneous preterm birth", Obstet Gynecol, 102, pp.850~856 (2003)
  27. Zhang J, "Blood pressure dynamics during pregnancy and spontaneous preterm birth", Am J Obstet Gynecol, 197, pp.162.e1~162.e6 (2007)
  28. O’Hara S, "Cervical length for predicting preterm birth and a comparison of ultrasonic measurement techniques", Australas J Ultrasound Med, 16, pp.124~134 (2013)
  29. Society for Maternal-Fetal Medicine(SMFM), "The role of routine cervical length screening in selected high-and low-risk women for preterm birth prevention", Am J Obstet Gynecol, 215, pp.B2~7 (2016)
  30. Berghella V, "Prior cone biopsy : prediction of preterm birth by cervical ultrasound", Am J Obstet Gynecol, 191, pp.1393~1397 (2004)
  31. Bevis KS, "Cervical conization and the risk of preterm delivery", Am J Obstet Gynecol, 205, pp.19~27 (2011)
  32. Pinborg A, "Cervical conization doubles the risk of preterm and very preterm birth in assisted reproductive technology twin pregnancies", Hum Reprod, 30, pp.197~204 (2015)
  33. 조수현, "Maternal Characteristics, Short Mid-Trimester Cervical Length, and Preterm Delivery", Journal of Korean Medical Science, 32(3), pp.488~494 (2017), 대한의학회
  34. Eke PI, "Update on prevalence of periodontitis in adults in the united states : NHANES 2009 to 2012", J Periodontol, 86, pp.611~622 (2015)
  35. Puertas A, "Association of periodontitis with preterm birth and low birth weight : a comprehensive review", J Matern Fetal Neonatal Med, 31, pp.597~602 (2018)
  36. Vakil N, "The Montreal definition and classification of gastroesophageal reflux disease : a global evidence-based consensus", Am J Gastroenterol, 101, pp.1900~1920 (2006)
  37. Patrick L, "Gastroesophageal reflux disease(GERD) : a review of conventional and alternative treatments", Altern Med Rev, 16, pp.116~133 (2011)
  38. Vinesh E, "A clinicopathologic study of oral changes in gastroesophageal reflux disease, gastritis, and ulcerative colitis", J Contemp Dent Pract, 17, pp.943~947 (2016)
  39. Deppe H, "Erosive esophageal reflux vs. non erosive esophageal reflux : oral findings in 71 patients", BMC Oral Health, 15, pp.84~ (2015)
  40. Ali RA, "Gastroesophageal reflux disease in pregnancy", Best Pract Res Clin Gastroenterol, 21, pp.793~806 (2007)
  41. Koivu A, "Predicting risk of stillbirth and preterm pregnancies with machine learning", Health Inf Sci Syst, 8, pp.14~ (2020)
  42. Fergus P, "Prediction of preterm deliveries from EHG signals using machine learning", PLoS One, 8, pp.e77154~ (2013)
  43. Sadi-Ahmed N, "Relevant features selection for automatic prediction of preterm deliveries from pregnancy ElectroHysterograhic(EHG)records", J Med Syst, 41, pp.204~ (2017)
  44. Krizhevsky A, "ImageNet classification with deep convolutional neural networks", Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, pp.1097~1105 (2018)
  45. Lee KS, "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, 19, pp.711~716 (2019)
  46. Gao C, "Deep learning predicts extreme preterm birth from electronic health records", J Biomed Inform, 100, pp.103334~ (2019)
  47. "Interpretable convolutional neural networks for preterm birth classification" (2019)[URL]
  48. Naimi AI, "Machine learning for fetal growth prediction", Epidemiology, 29, pp.290~298 (2018)
  49. Fung R, "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, 2, pp.e368~e375 (2020)
  50. Signorini MG, "Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring", Comput Methods Programs Biomed, 185, pp.105015~ (2020)
  51. Pini N, "A machine learning approach to monitor the emergence of late intrauterine growth restriction", Front Artif Intell, 4, pp.622616~ (2021)
  52. Lee KS, "Prediction of newborn’s body mass index using nationwide multicenter ultrasound data : a machinelearning study", BMC Pregnancy Childbirth, 21, pp.172~ (2021)
  53. Sridar P, "Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks", Ultrasound Med Biol, 45, pp.1259~1273 (2019)
  54. Venkatesh KK, "Machine learning and statistical models to predict postpartum hemorrhage", Obstet Gynecol, 135, pp.935~944 (2020)
  55. Asali A, "Intrahepatic cholestasis of pregnancy : machinelearning algorithm to predict elevated bile acid based on clinical and laboratory data", Arch Gynecol Obstet, 304, pp.641~647 (2021)
  56. Betts KS, "Predicting common maternal postpartum complications : leveraging health administrative data and machine learning", BJOG, 126, pp.702~709 (2019)
  57. Tsur A, "Development and validation of a machine-learning model for prediction of shoulder dystocia", Ultrasound Obstet Gynecol, 56, pp.588~596 (2020)
  58. Guedalia J, "Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning : a retrospective cohort study", BJOG, 128, pp.1824~1832 (2021)
  59. Guedalia J, "Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries", Am J Obstet Gynecol, 223, pp.437.e1~437.e15 (2020)
  60. Meyer R, "Implementation of machine learning models for the prediction of vaginal birth after cesarean delivery", J Matern Fetal Neonatal Med (2020)[DOI]
  61. Liu LC, "Concordance analysis of intrapartum cardiotocography between physicians and artificial intelligence-based technique using modified one-dimensional fully convolutional networks", J Chin Med Assoc, 84, pp.158~164 (2021)
  62. Zhong W, "A deep learning approach for fetal QRS complex detection", Physiol Meas, 39, pp.045004~ (2018)
  63. Drukker L, "Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology", Ultrasound Obstet Gynecol, 56, pp.498~505 (2020)
  64. Garcia-Canadilla P, "Machine learning in fetal cardiology : what to expect", Fetal Diagn Ther, 47, pp.363~372 (2020)
  65. Miyagi Y, "Recognition of facial expression of fetuses by artificial intelligence(AI)", J Perinat Med, 49, pp.596~603 (2021)
  66. Beksaç MS, "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, 59, pp.131~136 (1995)
  67. Kojita Y, "Deep learning model for predicting gestational age after the first trimester using fetal MRI", Eur Radiol, 31, pp.3775~3782 (2021)
  68. Torrents-Barrena J, "Assessment of radiomics and deep learning for the segmentation of fetal and maternal anatomy in magnetic resonance imaging and ultrasound", Acad Radiol, 28, pp.173~188 (2021)
  69. Looney P, "Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning", JCI insight, 3, pp.e120178~ (2018)
  70. Morris SA, "Deep learning for detecting congenital heart disease in the fetus", Nat Med, 27, pp.764~765 (2021)
  71. Gnanadass I, "Prediction of gestational diabetes by machine learning algorithms", IEEE Potentials, 39, pp.32~37 (2020)
  72. Wu YT, "Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning", J Clin Endocrinol Metab, 106, pp.e1191~e1205 (2021)
  73. Hoffman MK, "A machine learning algorithm for predicting maternal readmission for hypertensive disorders of pregnancy", Am J Obstet Gynecol MFM, 3, pp.100250~ (2021)
  74. Yang L, "Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm", Technol Health Care, 28(S1), pp.181~186 (2020)
  75. Munetoshi Akazawa, "The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study", Obstetrics & Gynecology Science, 64(3), pp.266~273 (2021), 대한산부인과학회[DOI]

관련 논문AI추천

이 간행물의 다른 논문 (12건)