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Obstetrics and Gynegology2022№12
Search for the predictors of...

Search for the predictors of fetal growth restriction: from a measuring tape to artificial intellect

DOI
https://dx.doi.org/10.18565/aig.2022.185

Gumeniuk Е.G., Ivshin A.A., Boldina Yu.S.

Department of Obstetrics and Gynecology, Dermatovenereology, Medical Institute, Petrozavodsk State University, Petrozavodsk, Russia
Fetal growth restriction (FGR) is a common obstetric pathology, the frequency of which in various populations may amount to as much as 5–15%. This pregnancy complication is associated with high perinatal morbidity and mortality rates, leading to serious complications for the fetus, newborn, and child.
The literature review presents a history of searching for FGR predictors from a measuring tape to artificial intellect. It discusses the importance of external fetometry, including clinical practice guidelines and Cochrane Reviews. There are data on the significance of ultrasonic fetometry. The review elucidates the limited role of some biomarkers in the first trimester screening program for the prediction and diagnosis of FGR. It analyzes a large number of risk factors and their heterogeneities that hinder the use of generally accepted statistical methods. There is a greater interest in the use of machine learning and artificial intelligence, including that in obstetrics and perinatology. Particular attention is given to the analysis and discussion of proposed models and algorithms for the prediction of FGR in recent years.
Conclusion: The dawning age of machine learning and artificial intelligence allows the prediction and timely diagnosis of FGR. Early prediction will facilitate personalized clinical monitoring and management, which will be able to improve fetal and newborn health.

Keywords

fetal growth restriction
external obstetric examination
ultrasonic fetometry
biomarkers
risk factors
prediction
machine learning
artificial intelligence
prediction
perinatal health
Full text (in Russian)

References

  1. Министерство здравоохранения Российской Федерации. Клинические рекомендации «Недостаточный рост плода, требующий предоставления медицинской помощи матери (задержка роста плода)». М.; 2022. 76 c. Доступно по: https://www.garant.ru/news/1537162/ [Ministry of Health of the Russian Federation. Clinical guidelines "Fetal growth deficiency requiring maternal medical care (fetal growth retardation)". М.; 2022. 76 p. (in Russian)]. Available at: https://www.garant.ru/news/1537162/
  2. Society for Maternal-Fetal Medicine Consult Series #52: Diagnosis and management of fetal growth restriction: (Replaces Clinical Guideline Number 3, April 2012). Am. J. Obstet. Gynecol. 2020; 223(4): B2-B17.https://dx.doi.org/10.1016/j.ajog.2020.05.010.
  3. Romero R. Prenatal medicine: the child is the father of the man. J. Matern. Fetal Neonatal Med. 2009; 22(8): 636-9. https://dx.doi.org/10.1080/14767050902784171.
  4. Di Renzo G.C. The great obstetrical syndromes. J. Matern. Fetal Neonatal Med. 2009; 22(8): 633-5. https://dx.doi.org/10.1080/14767050902866804.
  5. Saleem T., Sajjad N., Fatima S., Habib N., Ali S.R., Qadir M. Intrauterine growth retardation - small events, big consequences. Ital. J. Pediatr. 2011; 37: 41. https://dx.doi.org/10.1186/1824-7288-37-41.
  6. Teng L.Y., Mattar C.N.Z., Biswas A., Hoo W.L., Saw S.N. Interpreting the role of nuchal fold for fetal growth restriction prediction using machine learning. Sci. Rep. 2022; 12(1): 3907. https://dx.doi.org/10.1038/s41598-022-07883-0.
  7. Подзолкова Н.М., Денисова Ю.В., Скворцова М.Ю., Денисова Т.В., Шовгенова Д.С. Синдром задержки роста плода: нерешенные вопросы стратификации рисков, ранней диагностики и акушерской тактики. Вопросы гинекологии, акушерства и перинатологии. 2021; 20(5): 76-86. [Podzolkova N.M., Denisova Yu.V., Skvortsova M.Yu., Denisova T.V., Shovgenova D.S. Fetal growth restriction: unresolved issues of risk stratification, early diagnosis, and obstetric management. Vopr. ginekol. akus. perinatol. (Gynecology, Obstetrics and Perinatology). 2021; 20(5): 76-86. (in Russian)]. https:/dx.doi.org/10.20953/1726-1678-2021-5-76-86.
  8. McCowan L., Horgan R.P. Risk factors for small for gestational age infants. Best Pract. Res. Clin. Obstet. Gynaecol. 2009; 23(6): 779-93. https://dx.doi.org/10.1016/j.bpobgyn.2009.06.003.
  9. Suhag A., Berghella V. Intrauterine growth restriction (IUGR): etiology and diagnosis. Curr. Obstet. Gynecol. Rep. 2013; 2: 102-11. https://dx.doi.org/10.1007/s13669-013-0041-z.
  10. https://medicine.en-academic.com/31521/maneuver
  11. Neilson J.P. Symphysis-fundal height measurement in pregnancy. Cochrane Database Syst. Rev. 1998; 1998(1): CD000944. https://dx.doi.org/10.1002/14651858.CD000944.
  12. Japaraj R.P., Ho J.J., Valliapan J., Sivasangari S. Symphysial fundal height (SFH) measment in pregnancy for detecting abnormal fetal growth. Cochrane Database Syst. Rev. 2015; 2015(9): CD008136. https://dx.doi.org/10.1002/14651858.CD008136.pub3.
  13. Министерство здравоохранения Российской Федерации. Клинические рекомендации «Нормальная беременность». М.; 2019. 88c.
  14. https://www.consultant.ru/document/cons_doc_LAW_343139/ [Ministry of Health of the Russian Federation. Clinical guidelines "Normal Pregnancy". М.; 2019. 88p. (in Russian)]. Available at: https://www.consultant.ru/document/cons_doc_LAW_343139/
  15. Marchand C., Köppe J., Köster H.A., Oelmeier K., Schmitz R., Steinhard J. et al. Fetal growth restriction: comparison of biometric parameters. J. Pers. Med. 2022; 12(7): 1125. https://dx.doi.org/10.3390/jpm12071125.
  16. Hadlock F.P., Harrist R.B., Carpenter R.J., Deter R.L., Park S.K. Sonographic estimation of fetal weight. The value of femur length in addition to head and abdomen measurements. Radiology. 1984; 150(2): 535-40.https://dx.doi.org/10.1148/radiology.150.2.6691115.
  17. Shepard M.J., Richards V.A., Berkowitz R.L., Warsof S.L., Hobbins J.C. An evaluation of two equations for predicting fetal weight by ultrasound. Am. J. Obstet. Gynecol. 1982; 142(1): 47-54. https://dx.doi.org/10.1016/s0002-9378(16)32283-9.
  18. Sereke S.G., Omara R.O., Bongomin F., Sarah Nakubulwa S., Kisembo H.N. Prospective verification of sonographic fetal weight estimators among term parturients in Uganda. BMC Pregnancy Childbirth. 2021; 21(1): 175.https://dx.doi.org/10.1186/s12884-021-03645-4.
  19. Melamed N., Baschat A., Yinon Y., Athanasiadis A., Mecacci F., Figueras F. et al. FIGO (international Federation of Gynecology and Obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction. Int. J. Gynaecol. Obstet. 2021; 152(Suppl. 1): 3-57. https://dx.doi.org/10.1002/ijgo.13522.
  20. Stepan H., Hund M., Andraczek T. Combining biomarkers to predict pregnancy complications and redefine preeclampsia: the angiogenic-placental syndrome. Hypertension. 2020; 75(4): 918-26. https://dx.doi.org/10.1161/HYPERTENSIONAHA.119.13763.
  21. Papastefanou I., Wright D., Lolos M., Anampousi K., Mamalis M.,Nicolaides K.H. Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics, serum pregnancy-associated plasma protein-A and placental growth factor at 11-13 weeks' gestation. Ultrasound Obstet. Gynecol. 2021; 57(3): 392-400. https://dx.doi.org/10.1002/uog.23118.
  22. Ormesher L., Warrander L., Liu Y., Thomas S., Simcox L., Smith G.C.S. et al. Risk stratification for early-onset fetal growth restriction in women with abnormal serum biomarkers: a retrospective cohort study. Sci. Rep. 2020; 10(1): 22259. https://dx.doi.org/10.1038/s41598-020-78631-5.
  23. Hendrix M., Bons J., van Haren A., van Kuijk S., van Doorn W., Kimenai D.M. et al. Role of sFlt-1 and PlGF in the screening of small-for-gestational age neonates during pregnancy: a systematic review. Ann. Clin. Biochem. 2020; 57(1): 44-58. https://dx.doi.org/10.1177/0004563219882042.
  24. Shinar S., Tigert M., Agrawal S., Parks W.A., Kingdom J.C. Placental growth factor as a diagnostic tool for placental mediated fetal growth restriction. Pregnancy Hypertens. 2021; 25: 123-8. https://dx.doi.org/10.1016/j.preghy.2021.05.023.
  25. Министерство здравоохранения Российской Федерации. Клинические рекомендации «Преэклампсия. Эклампсия. Отеки, протеинурия и гипертензивные расстройства во время беременности, в родах и послеродовом периоде». М.; 2021. 79c. https://www.consultant.ru/document/cons_doc_LAW_388618/ [Ministry of Health of the Russian Federation. Clinical guidelines "Preeclampsia. Eclampsia. Edema, proteinuria and hypertensive disorders during pregnancy, childbirth and the postpartum period. М.; 2021. 79p. (in Russian)]. Available at: https://www.consultant.ru/document/cons_doc_LAW_388618/
  26. Клычева О.И., Хурасева А.Б. Возможности прогнозирования степени риска развития синдрома задержки роста плода. Российский вестник акушерства и гинекологии. 2020; 20(5): 68-73. https://dx.doi.org/10.17116/rosakush20202005168. [Klycheva O.I., Khuraseva A.B. Possibilities for predicting the risk of developing fetal growth retardation syndrome. Russian Bulletin of Obstetrician-Gynecologist. 2020;20(5):68 73. (in Russian)].https://dx.doi.org/10.17116/rosakush20202005168.
  27. Яворская С.Д., Долгова Н.Г., Фадеева Н.И., Ананьина Л.П. Материнские клинико-анамнестические факторы формирования задержки роста плода. Вопросы гинекологии, акушерства и перинатологии. 2019; 18(5): 83-7. https://dx.doi.org/10.20953/1726-1678-2019-5-83-87. [Yavorskaya S.D., Dolgova N.S., Fadeeva N.I., Ananyina L.P. Maternal clinical and anamnestic factors of fetal growth retardation. Issues of Gynecology, Obstetrics and Perinatology. 2019; 18(5): 83-87. (in Russian)].https://dx.doi.org/10.20953/1726-1678-2019-5-83-87.
  28. Bachmann L.M., Khan K.S., Ogah J., Owen P. Multivariable analysis of tests for the diagnosis of intrauterine growth restriction. Ultrasound Obstet. Gynecol. 2003; 21(4): 370-4. https://dx.doi.org/10.1002/uog.77.
  29. Kuhle S., Maguire B., Zhang H., Hamilton D., Allen A.C., Joseph K.S., Allen V.M. Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. BMC Pregnancy Childbirth. 2018; 18(1): 333. https://dx.doi.org/10.1186/s12884-018-1971-2.
  30. Rashidi H.H., Tran N.K., Betts E.V., Howell L.P., Green R. Artificial Intelligence and machine learning in pathology: the present landscape of supervised methods. Acad. Pathol. 2019; 6: 2374289519873088.https://dx.doi.org/10.1177/2374289519873088.
  31. Iftikhar P., Kuijpers M.V., 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): 7124. https://dx.doi.org/10.7759/cureus.7124.
  32. Ramakrishnan R., Rao S., He J.R. Perinatal health predictors using artificial intelligence: a review. Womens Health (London). 2021; 17: 1-7.https://dx.doi.org/10.1177/17455065211046132.
  33. Bertini A., Salas R., Chabert S., Sobrevia L., Pardo F. Using machine learning to predict complications in pregnancy: a systematic review. Front. Bioeng. Biotechnol. 2022; 9: 780389. https://dx.doi.org/10.3389/fbioe.2021.780389.
  34. Shazly S.A., Trabuco E.C., Ngufor C.G., Famuyide A.O. Introduction to machine learning in obstetrics and gynecology. Obstet. Gynecol. 2022; 139(4): 669-79. https://dx.doi.org/10.1097/AOG.0000000000004706.
  35. Ahn K.H., Lee K.S. Artificial intelligence in obstetrics. Obstet. Gynecol. Sci. 2022; 65(2): 113-24. https://dx.doi.org/10.5468/ogs.21234.
  36. Naimi A.I., Platt R.W., Larkin J.C. Machine learning for fetal growth prediction. Epidemiology. 2018; 29(2): 290-8. https://dx.doi.org/10.1097/EDE.0000000000000788.
  37. Signorini M.G., 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.https://dx.doi.org/10.1016/j.cmpb.2019.105015.
  38. Fung R., Villar J., Dashti A., Ismail L.C., Staines-Uria E., Ohuma E.O. 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(7): e368-75. https://dx.doi.org/10.1016/S2589-7500(20)30131-X.
  39. Pini N., Lucchini M., Esposito G., Tagliaferri S., Campanile M., Magenes G., Signorini M.G. A machine learning approach to monitor the emergence of late intrauterine growth restriction. Front. Artif. Intell. 2021; 4: 622616.https://dx.doi.org/10.3389/frai.2021.622616.
  40. Crockart I.C., Brink L.T., du Plessis C., Odendaal H.J. Classification of intrauterine growth restriction at 34-38 week’s gestation with machine learning models. Inform. Med. Unlocked. 2021; 23: 100533. https://dx.doi.org/10.1016/j.imu.2021.100533.
  41. Lee K.S., Kim H.Y., Lee S.J., Kwon S.O., Na S., Hwang H.S. et al. Prediction of newborn’s body mass index using nationwide multicenter ultrasound data: a machinelearning study. BMC Pregnancy Childbirth. 2021; 21(1): 172.https://dx.doi.org/10.1186/s12884-021-03660-5.
  42. Tao J., Yuan Z., Sun L., Yu K., Zhang Z. Fetal birthweight prediction with measured data by a temporal machine learning method. BMC Med. Inform. Decis. Mak. 2021; 21(1): 26. https://dx.doi.org/10.1186/s12911-021-01388-y.
  43. Saw S.N., Biswas A., Mattar C.N.Z., Lee H.K., Yap C.H. Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor. Prenat. Diagn. 2021; 41(4): 505-16. https://dx.doi.org/ 10.1002/pd.5903.
  44. Burgos-Artizzu X.P., Coronado-Gutiérrez D., Valenzuela-Alcaraz B., Vellvé K., Eixarch E., Crispi F. 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://dx.doi.org/10.1016/j.ajogmf.2021.100462.
  45. Bahado-Singh R.O., Yilmaz A., Bisgin H., Turkoglu O., Kumar P., Sherman E. et al. Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction. PLoS One. 2019; 14(4): e0214121. https://dx.doi.org/10.1371/journal.pone.0214121.
  46. Ившин А.А., Гусев А.В., Новицкий Р.Э. Искусственный интеллект: предиктивная аналитика перинатального риска. Вопросы гинекологии, акушерства и перинатологии. 2020; 19(6): 133-44. https://dx.doi.org/10.20953/1726-1678-2020-6-133-144. [Ivshin A.A., Gusev A.V., Novitskiy R.E. Artificial intelligence: predictive analytics of perinatal risks. Issues of Gynecology, Obstetrics and Perinatology. 2020; 19(6): 133-144. (in Russian)].https://dx.doi.org/10.20953/1726-1678-2020-6-133-144.
  47. Сухих Г.Т., Давыдов Д.Г., Логинов В.В., Баев О.Р., Приходько А.М., Шешко Е.Л., Чмыхова Е.В. Состояние и перспективы внедрения технологий искусственного интеллекта в акушерско-гинекологическую практику. Акушерство и гинекология. 2021; 2: 5-12. https://dx.doi.org/10.18565/aig.2021.2.5-12. [Sukhikh G.T., Davydov D.G., Loginov V.V., Baev O.R., Prikhodko A.M., Sheshko E.L., Chmykhova E.V. The state of and prospects for the introduction of artificial intelligence technologies in obstetric and gynecological practice. Obstetrics and Gynecology. 2021; 2: 5-12. (in Russian)]. https://dx.doi.org/10.18565/aig.2021.2.5-12.

Received 08.08.2022

Accepted 11.10.2022

About the Authors

Elena G. Gumeniuk, Dr. Med. Sci, Professor, Professor of the Department of Obstetrics and Gynecology, Dermatovenerology of the Medical Institute, Petrozavodsk State University; Chairman of the Karelian Association of Obstetricians and Gynecologists, +7(909)567-12-51, https://orcid.org/0000-0001-7834-096X,
31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia.
Alexander A. Ivshin, PhD, Associate Professor, Head of the Department of Obstetrics and Gynecology, Dermatovenerology of the Medical Institute, Petrozavodsk State University, +7(909)567-12-51, https://orcid.org/0000-0001-7834-096X, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia.
Yuliya S. Boldina, Assistant, Department of Obstetrics and Gynecology and Dermatovenerology of the Medical Institute, Graduate Student, Petrozavodsk State University,
+7(981)405-85-24, https://orcid.org/0000-0002-1450-650X, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia.
Corresponding author: Alexander A. Ivshin, scipeople@mail.ru

Authors’ contributions: Gumeniuk Е.G. – design, search, analysis, and translation of literature, writing and editing the literature review; Ivshin A.A. – idea, participation in the writing and editing the scientific review; Boldina Yu.S. – participation in the writing and editing the scientific review.
Conflicts of interest: The authors declare that there are no conflicts of interest.
Funding: The investigation has been financially supported by the Ministry of Science and Higher Education of the Russian Federation under Agreement No. 075-15-2021-665; the investigation has been conducted using a unique scientific attitude (USA) “Multicomponent software and hardware complex for automated collection, storage, labeling of research and clinical biomedical data, their unification and analysis based on the data center, by applying artificial intelligence technologies” (Registration Number 2075518).
For citation: Gumeniuk Е.G., Ivshin A.A., Boldina Yu.S.
Search for the predictors of fetal growth restriction:
from a measuring tape to artificial intellect.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2022; 12: 18-24 (in Russian)
https://dx.doi.org/10.18565/aig.2022.185

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