SpringerLink
Log in

Second Trimester and Artificial Intelligence

  • Chapter
  • First Online:
Pregnancy with Artificial Intelligence

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 234))

  • 267 Accesses

Abstract

In this chapter we are going to explore the more complex anatomy of the second trimester fetus. Once again, Artificial Intelligence systems can aid the sonographer by signaling possible anomalies. Congenital heart diseases can also be spotted using the sonographer + Artificial Intelligence merger. Other screening tests are also depicted.

Families with babies and families without babies are sorry for each other

Ed Lowe

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Donald, I., Macvicar, M.B., Brown, T.G.: Investigation of abdominal masses by pulsed ultrasound. Lancet 271(7032), 1188–1195 (1958). https://doi.org/10.1016/S0140-6736(58)91905-6

    Article  Google Scholar 

  2. King, H.: Agnodike and the profession of medicine. Proc. Camb. Philol. Soc. Ser. 32(212), 53–77 (1986). https://www.jstor.org/stable/44696917

  3. Gabbe, S.G., Niebyl, J.R., Simpson, J.L., Landon, M.B., Galana, H.L., Jauniaux, E.R.M., Driscoll, D.A.: Obstetrics, Normal and Problem Pregnancies. Elsevier, Amsterdam (2012)

    Google Scholar 

  4. Jalal, H.Q., Ismail, S.K.: Is there an association between body mass index and cervical length? Current Res. Diabet. Obes. J. 12(1), 555829 (2019). https://doi.org/10.19080/CRDOJ.2019.11.555829

    Google Scholar 

  5. Kandeel, M.S., Sanad, Z.F., Sayyed, T.M., Elmenawy, S.G.A.E.: The effect of body mass index on cervical characteristics and on the length of gestation in low-risk pregnancies. Menoufia Med. J. 27(3), 518–523 (2014)

    Article  Google Scholar 

  6. Farinelli, C.K., Wing, D.A., Szychowski, J.M., Owen, J., Hankins, G., Iams, J.D., Sheffield, J.S., Perez-Delboy, A., Berghella, V., Guzman, E.R.: Association between body mass index and pregnancy outcome in a randomized trial of cerclage for short cervix. Ultrasound Obstet. Gynecol. 40(6), 669–673 (2012). https://doi.org/10.1002/uog.11170

    Article  Google Scholar 

  7. Poggi, S.H., Vyas, N.A., Pezzullo, J.C., Landy, H.J., Ghidini, A.: Does increasing body mass index affect cerclage efficacy? J. Perinatol. 32, 777–779 (2012). https://doi.org/10.1038/jp.2011.198

    Article  Google Scholar 

  8. Oppenheimer, D.M., Monin, B.: The retrospective gambler’s fallacy: unlikely events, constructing the past, and multiple universes. Judgement Decis. Making 4(5), 326–334 (2009)

    Google Scholar 

  9. Nunnari, S., Zapal, J.: Gambler’s fallacy and imperfect best response in legislative bargaining. Games Econ. Behav. 99(C), 275–294 (2016). https://doi.org/10.1016/j.geb.2016.06.008

  10. McKay, B., Bar-Natan, D., Bar-Hillel, M., Kalai, G.: Solving the Bible code puzzle. Stat. Sci. 14(2), 150–173 (1999)

    Google Scholar 

  11. Belciug, S.: Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment. Elsevier, Amsterdam (2020)

    Google Scholar 

  12. Lu, Y., Zhang, X., Fu, X., Chen, F., Wong, K.K.L.: Ensemble machine learning for estimating fetal weight at varying gestational age. Proc. AAAI Conf. Artif. Intel. 33(01), 9522–9527 (2019). https://doi.org/10.1609/aaai.v33i01.33019522

  13. Solt, I., Caspi, O., Beloosesky, R., Weiner, Z., Avdor, E.: Machine learning approach to fetal weight estimation. Am. J. Obstet. Gynecol. 220(1), S666–S667 (2019). https://doi.org/10.1016/j.ajog.2018.11.1063

    Article  Google Scholar 

  14. Cheng, Y.C., Yan, G.L., Chiu, Y.H., Chang, F.M., Chang, C.H., Chung, K.C.: Efficient fetal size classification combined with artificial neural network for estimation of fetal weight. Taiwan J. Obstet. Gynecol. 51(4), 545–553 (2012). https://doi.org/10.1016/j.tjog.2012.09.009

    Article  Google Scholar 

  15. Miyagi, Y., Miyake, T.: Potential of artificial intelligence for estimating Japanese fetal weights. Acta Med. Okayama 74(6), 483–493 (2020). https://doi.org/10.18926/AMO/61207

    Google Scholar 

  16. Lebit, F.D., Vladareaunu, R.: The role of 4D ultrasound the assessment of fetal behaviour. Maedica (Bucur) 6(2), 120–127 (2011)

    Google Scholar 

  17. Agathokleous, M., Chaveeva, P., Poon, L.C.Y., Kosinski, P., Nicolaides, H.: Meta-analysis of second-trimester markers for trisomy 21. Ultrasound Obstet. Gynecol. 41(3), 247–261 (2013). https://doi.org/10.1002/uog.12364

    Article  Google Scholar 

  18. Nemescu, D., Onofriescu, M.: Factors affecting the feasibility of routine first-trimester fetal echocardiography. J. Ultrasound Med. 34(1), 161–166 (2015). https://doi.org/10.7863/ultra.34.1.161

    Article  Google Scholar 

  19. Respondek-Liberska, M.: Missed diagnosis in prenatal evaluation by ultrasound: a retrospective analysis of four cases from a tertiary center for fetal malformation. Prenat Cardio 6(1), 56–66 (2016)

    Article  Google Scholar 

  20. Tegnander, E., Eik-Nes, S.H.: The examiner’s ultrasound experience has a significant impact on the detection rate of congenital heart defect at the second trimester fetal examination. Ultrasound Obstet. Gynecol. 28, 8–14 (2006)

    Article  Google Scholar 

  21. Paladini, D.: Sonography in obese and overweight pregnant women: clinical medicolegal and technical issues. Ultrasound Obstet Gynecol. 33(6), 720–729 (2009)

    Article  Google Scholar 

  22. Tibbals, J., Cantwell-Barti, A.: Outcomes of management decisions by parents for their infants with hypoplastic left heart syndrome born with an without a prenatal diagnosis. J. Paediatr. Child Health 44, 321–324 (2008)

    Article  Google Scholar 

  23. Franklin, O., Burch, M., Manning, N., Sleeman, K., Gould, S., Archer, N.: Prenatal diagnosis of coarctation of the aorta improves survival and reduces morbidity. Heart 87, 67–69 (2002)

    Article  Google Scholar 

  24. Archerman, R.J., Evan, W.N., Luna, C.F., Rollins, R., Kip, K.T., Collazos, J.C., Restrepo, H., Adascheck, J., Iriye, B.K., Roberts, D., Sacks, A.J.: Prenatal detection of congenital heart disesase in southern Nevada: the need for universal fetal cardiac evaluation. J. Ultrasound Med. 26, 1715–1719 (2007)

    Article  Google Scholar 

  25. Mahle, W.T., Clancy, R.R., McGaurn, S.P., Goin, J.E., Clack, B.J.: Impact of prenatal diagnosis on survival and early neurologic morbidity in neonates with hypoplastic left heart syndrome. Pediatrics 107, 1277–1282 (2001)

    Article  Google Scholar 

  26. Schultz, A.H., Localio, A.R., Clarck, B.J., Ravinshakar, C., Videon, N., Kimmel, S.E.: Epidemiologic features of the presentation of critical congenital heart disease: implications for screening. Pediatrics 121, 751–757 (2008)

    Article  Google Scholar 

  27. Brown, K.L., Ridout, D.A., Hoskote, A., Verhults, K., Ricci, M., Bull, C.: Delayed diagnosis of congenital heart diseases worsens preoperative condition and outcome of surgery in neonates. Heart 92, 1298–1302 (2006)

    Article  Google Scholar 

  28. Tworetzky, W., McElhinney, D.B., Reddy, V.M., Brook, M.M., Hanley, F.L., Silverman, N.H.: Improved surgical outcome after fetal diagnosis of hypoplastic left heart syndrome. Circulation 103, 1269–1273 (2001)

    Article  Google Scholar 

  29. Verheijen, P.M., Lisowski, L.A., Stoutenbeek, P., Hitchcock, J.F., Bennink, G.K., Miejboom, E.J.: Lactocidosis in neonate is minimized by prenatal detection of congenital heart disease. Ulstrasound Obstet. Gynecol. 19, 552–555 (2002)

    Article  Google Scholar 

  30. Kumar, R.K., Newburger, J.W., Gauvrau, K., Kamenir, S.A., Hornberger, L.K.: Comparison of outcome when hypoplastic left heart syndrome and transposition of the great arteries are diagnosed prenatally versus when diagnosis of these two conditions is made only postnatally. Am. J. Cardiol. 83, 1649–1653 (1999)

    Article  Google Scholar 

  31. Bonnet, D., Coltri, A., Butera, G., Fermont, L., Le Bidois, J., Kachaner, J., Sisi, D.: Detection of transposition of the great arteries in fetuses reduces neonatal morbidity and mortality. Circulation 88, 916–918 (1999)

    Article  Google Scholar 

  32. Bensemlali, M., Stirnemann, J., Le Bidois, J., Levy, M., Raimondi, F., Hery, E., Stos, B., Sessieres, B., Boudjemline, Y., Bonnet, D.: Discordances between pre-natal and post-natal diagnosis of congenital heart diseases and impact on care strategies. J. Am. Coll. Cardiol. 68, 921–930 (2016)

    Google Scholar 

  33. Mozumbar, N., Rowland, J., Pan, S., Rajagopal, H., Geiger, M.K., Srivastava, S., Stern, K.W.D.: Diagnostic accuracy of fetal echocardiography in major congenital heart disease. J. Am. College Cardiol. 73(8), 1 (2019). https://doi.org/10.1016/S0735-1097(19)31197-0

    Google Scholar 

  34. Janczewska, I., Domzalska–Popadiuk, I., Swiatek-Brzezinski, Z.: Prenatal echocardiography-the impact on neonatal management. Signa Vitae 14(2), 51–60 (2018)

    Google Scholar 

  35. Belotti, M., Fesslovam, V., de Gasperi, C., Rognoni, G., Bee, V., Zucca, I., Cappellini, A., Byltamante, G., Lombardi, C.M.: Reliability of the first-trimester cardiac scan by ultrasound-trained obstetricians with high-frequency transabdominal probes in fetuses with increased nuchal transluceny. Ulstrasound Obstet. Gynecol. 36, 272–278 (2010)

    Article  Google Scholar 

  36. Lombardi, C.M., Bellotti, M., Fesslova, V., Cappellini, A.: Fetal echocardiograohy at the time of the nuchal transluceny, scan. Ultrasound Obstet. Gynecol. 19, 360–365 (2002)

    Google Scholar 

  37. Bronshtein, M., Zimmer, E.Z.: The sonographic approach to the detection of fetal cardiac anomalies in early pregnancy. Ultrasound Obstet. Gynecol. 19, 360–365 (2002)

    Article  Google Scholar 

  38. Yoo, S.J., Lee, Y.H., Kim, E.S., Ryu, H.M., Kim, M.Y., Choi, H.K., Cho, K.S., Kim, A.: Three-vesel view of the fetal upper mediastinum: an easy means of detecting abnormalities of the ventricular outflow tract and great arteries during obstetric screening. Ultrasound Obstet. Gynecol. 9, 173–182 (1997)

    Article  Google Scholar 

  39. Abu-Sulaiman, R.M., Subaih, B.: Congenital heart disease in infants of diabetic mothers: echocardiographic study. Pediatr. Cardiol. 25(2), 137–140 (2004). https://doi.org/10.1007/s00246-003-0538-8

    Article  Google Scholar 

  40. Komatsu, M., Sakai, A., Komatsu, R., Matsuoka, R., Yasutomi, S., Shozu, K., Dozen, A., Machino, H., Hidaka, H., Arakaki, T., Asada, K., Kaneko, S., Sekikawa, A., Hamamoto, R.: Detection of cardiac structural abnormalities in fetal ultrasound videos using Deep Learning. Appl. Sci. 11(1), 371 (2021). https://doi.org/10.3390/app11010371

    Article  Google Scholar 

  41. Sakai, A., Komatsu, M., Komatsu, R., Matsuoka, R., Yasutomi, S., Dozen, A., Shozu, K., Arakaki, T., Machino, H., Asada, K., Kaneko, S., Sekizawa, A., Hamamoto, R.: Medical professional enhancement using explainable artificial intelligence in fetal cardiac ultrasound screening. Biomedicines 10, 551 (2022). https://doi.org/10.3390/biomedicines,10030551

    Article  Google Scholar 

  42. Komatsu, R., Matsuoka, R., Arakaki, T., Tokunaka, M., Komatsu, M., Sakai, A.: Novel AI-guided ultrasound screening system for fetal heart that can demonstrate findings in timeline diagram. Ultrasound Obstet. Gynecol. (2019). https://doi.org/10.1002/uog.20796

    Article  Google Scholar 

  43. Arnaout, R., Curran, L., Zhao, Y., Levine, J., Chinn, E., Moon-Grady, A.: Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning. medRxiv (2020). https://doi.org/10.1101/2020.06.22.20137786

  44. Le, T.K., Truong, V., Nguyen-Vo, T.-H., Nguyen, B.P., Ngo, T.N.M., Bui, Q.V.P., Pham, T.K.N., Tretter, J., Taylor, M., Levy, P., Chung, E., Mazur, W., Do, H.Q., Do, P.T.N., Pham, V.N., Chau, H.A.: Application of machine learning in screening of congenital heart disease using fetal echocardiography. J. Am. Coll. Cardiol. 75(11), 648 (2020)

    Article  Google Scholar 

  45. McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MathSciNet  MATH  Google Scholar 

  46. Hebb, D.O.: The Organization of Behavior. A Neuropsychological Theory. Wiley, New York (1949)

    Google Scholar 

  47. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. v65(6), 386–408 (1958). https://doi.org/10.10137/h0042519

  48. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  MATH  Google Scholar 

  49. Smith, L.N.: Cyclical learning rates for training neural networks. In: IEEE Winter Conference on Computer Vision Applications (WACV) (2017). https://doi.org/10.1109/WACV.2017.58

  50. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications in Biology, Control and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  51. Darwin, C.: On the Origin of Species by Means of Natural Selection, or Preservation of Favored Races in the Struggle of Life. John Murray, London (1859)

    Google Scholar 

  52. Radcliffe, N.J.: Genetic Neural Network on MIMD Computers (Unpublished D. Phil Thesis). University of Edinburg, Edinburg (1990)

    Google Scholar 

  53. Watson, R.A., Pollack, J.B.: Recombination without respect: schema combination and disruption in genetic algorithm crossover. In: Whitley D., Golberg, E.C.-P., Spector, L., Parmee, I., Beyer, H.-G. (eds.) Proceedings of the 2000 Genetic and Evolutionary Computation Conference, Morgan Kaufmann, San Mateo, CA, pp 112–119 (2000)

    Google Scholar 

  54. Schaffer, J.D., Whitley, D.L., Eshelman, L.J.: Combinations of genetic algorithms and neural networks: a survey of the state-of-the art. In: Whitley, D.L., Schaffer, J.D. (eds.) Proceedings of International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp. 1–37. IEEE Computer Society, Los Alamitos CA (1992)

    Google Scholar 

  55. Hancock, P.J.B.: Genetic algorithm and permutation problems: a comparison of recombination operators for neural net structure specification. In: Whitley, D.L., Schaffer, J.D. (eds.) Proceedings of International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp. 108–122. IEEE Computer Society, Los Alamitos CA (1992)

    Google Scholar 

  56. Whitley, D.L., Starkweather, T., Bogart, C.: Genetic algorithm and neural networks: optimizing connection and connectivity. Parallel Comput. 14(3), 347–361 (1990)

    Article  Google Scholar 

  57. Blickle, T., Thiele, K.: A Comparison of Selection Schemes Used in Genetic Algorithms (TIK-Report), vol. 11 (1995)

    Google Scholar 

  58. Jebari, K., Madiagi, M.: Selection methods for genetic algorithm. Int. J. Emerg. Sci. 3(4), 333–344 (2013)

    Google Scholar 

  59. Razali, N.M., Geraghty, J.: Genetic algorithm performance with different selection strategies in solving TSP. In: Proceedings of the World Congress on Engineering, vol. II, UK (2011)

    Google Scholar 

  60. Eckhardt, R., Ulman, S., von Neumann, J.: Monte Carlo method. Los Alamos, Sci. Special Issue, pp. 131–141 (1987)

    Google Scholar 

  61. Metropolis, N.: The beginning of the Monte Carlo method. Los Alamos, Sci. Special Issue, pp. 124–130 (1987)

    Google Scholar 

  62. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heildelberg (2003)

    Book  MATH  Google Scholar 

  63. Eiben, A.E., Smith, J.E.: Multiparent recombination in evolutionary computing. In: Gosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computations: Theory and Applications, pp. 175–192. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  64. Watson, R.A., Pollack, J.B.: Recombination without respect: schema combination and disruption in genetic algorithm crossover. In: Whitley, D., Golbder, E.C.-P., Spector, K., Parmee, I., Beyer, H.-G. (eds.) Proceedings of the 2000 Genetic and Evolutionary Computation Conference. Morgan Kaufmann, San Mateo, CA, pp. 112–119 (1963)

    Google Scholar 

  65. Eschelman, L., Schaffer, D.J.: Real-coded genetic algorithms and interval schemata. Found. Genet. Algorithms. 2, 187–202 (1993). https://doi.org/10.1016/B978-0-08-094832-4.50018-0

  66. Wright, A.: Genetic algorithms for real parameter optimization. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, pp. 205–218 (1991)

    Google Scholar 

  67. Belciug, S., Gorunescu, F.: A hybrid neural network/genetic algorithm system applied to the breast cancer detection and recurrence. Exp. Syst. J. Knowl. Eng. 30(3), 243–254 (2013)

    Google Scholar 

  68. Hajek, A.: Interpretation of probability. In: Edward, N.Z. (ed.) The Sandford Encyclopedia of Philosophy. Winter. https://plato.standford.edu/archives.win2012/entries/probability-interpret/

  69. Press, J.: Subjective and Objective Bayesian Statistics: Principles, Models, and Applications, 2nd ed. Wiley. https://onlinelibrary.wiley.com; https://doi.org/10.1002/9780470317105.fmatter/pdf

  70. Wagenmakers, E.-J., Lee, M., Lodewckx, T., Iverson, G.: Bayesian evaluation of informative hypotheses (statistics for social and behavioral science). In: Hoijtink, H., Klugkist, I., Boelen, P. (eds.) Bayesian Versus Frequentist Interference, pp. 181–207. Springer, Berlin (2008)

    Google Scholar 

  71. Belciug, S., Gorunescu, G.: Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high-dimensional gene expression and proteomic spectra data sets in cancer detection. J. Biomed. Inform. 83, 159–166 (2018). https://doi.org/10.1016/j.jbi/2018.06.003

    Article  Google Scholar 

  72. Belciug, S.: Logistic regression paradigm for training a single-hidden layer feedforward neural network. Application to gene expression datasets for cancer research. J. Biomed. Inform. 102, 103372 (2020). https://doi.org/10.1016/j.jbi.2019.103372

  73. Belciug, S.: Parallel versus cascaded logistic regression trained single-hidden feedforward neural network for medical data. Exp. Syst. Appl. 170, 114538 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Science, University of Craiova, Craiova, Dolj, Romania

    Smaranda Belciug

  2. Department of Obstetrics and Gynaecology, University of Medicine and Pharmacy of Craiova, Craiova, Dolj, Romania

    Dominic Iliescu

Authors
  1. Smaranda Belciug
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dominic Iliescu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Smaranda Belciug .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Belciug, S., Iliescu, D. (2023). Second Trimester and Artificial Intelligence. In: Pregnancy with Artificial Intelligence. Intelligent Systems Reference Library, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-031-18154-2_4

Download citation

Publish with us

Policies and ethics

Search

Navigation