PREDICTING INTRAUTERINE GROWTH RESTRICTION: A PILOT STUDY WITH FEED-FORWARD BACK PROPAGATION NETWORK

Authors

  • Ioana-Sadyie SCRIPCARIU “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • Ingrid-Andrada VASILACHE “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • Ioana PAVALEANU “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • B. DOROFTEI “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • A. CARAULEANU “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • Demetra SOCOLOV “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • Alina-Sinziana MELINTE-POPESCU “Dunărea de Jos” University Galaţi / Faculty of Medicine and Pharmacy
  • Petronela VICOVEANU “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • V. HARABOR “Stefan cel Mare” University, Suceava
  • Elena MIHALCEANU “Grigore T. Popa” University of Medicine and Pharmacy Iasi
  • M. MELINTE-POPESCU “Dunărea de Jos” University Galaţi / Faculty of Medicine and Pharmacy
  • Anamaria HARABOR “Stefan cel Mare” University, Suceava
  • Mariana STUPARU-CRETU “Stefan cel Mare” University, Suceava
  • D. NEMESCU “Grigore T. Popa” University of Medicine and Pharmacy Iasi

Abstract

The prediction of intrauterine growth restriction (IUGR) represents a challenge for obstetricians throughout pregnancy, and the use of artificial intelligence could improve its screening. The aim of this pilot study was to prospectively design and test a Feed-Forward Backpropagation neural network (FFBPN) for the prediction of IUGR and its subtypes. Materials and methods: Between January and September 2023, we included 108 pregnant patients with singleton pregnancies who underwent conventional first trimester screening. Clinical and paraclinical data was used to construct a FFBPN, and its predictive performance was assessed using a sensitivity analysis. Results: Our results indicated that the FFBPN predicted the development of IUGR during pregnancy with a sensitivity (Se) of 94.7%, specificity (Sp) of 97.7%, and a false positive rate of 2%. The value of the area under the curve for this neural network was 0.978. On the other hand, when used for the prediction of IUGR subtypes, the FFBPN achieved lower predictive performance. Also, the sensitivity analysis revealed that the FFBPN could better predict early IUGR, with a Se of 71.4%, Sp of 94%, and accuracy of 90.7%.

Author Biographies

  • Ioana-Sadyie SCRIPCARIU, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • Ingrid-Andrada VASILACHE, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • Ioana PAVALEANU, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • B. DOROFTEI, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • A. CARAULEANU, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • Demetra SOCOLOV, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • Alina-Sinziana MELINTE-POPESCU, “Dunărea de Jos” University Galaţi / Faculty of Medicine and Pharmacy

    Faculty of Medicine and Pharmacy

  • Petronela VICOVEANU, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • V. HARABOR, “Stefan cel Mare” University, Suceava

    Faculty of Medicine and Biological Sciences

  • Elena MIHALCEANU, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

  • M. MELINTE-POPESCU, “Dunărea de Jos” University Galaţi / Faculty of Medicine and Pharmacy

    Faculty of Medicine and Pharmacy

  • Anamaria HARABOR, “Stefan cel Mare” University, Suceava

    Faculty of Medicine and Biological Sciences

  • Mariana STUPARU-CRETU, “Stefan cel Mare” University, Suceava

    Faculty of Medicine and Biological Sciences

  • D. NEMESCU, “Grigore T. Popa” University of Medicine and Pharmacy Iasi

    Faculty of Medicine, Department of Mother and Child Medicine

References

1. Gaccioli F, Aye I, Sovio U, Charnock-Jones DS, Smith GCS. Screening for fetal growth restriction using fetal biometry combined with maternal biomarkers. Am J Obstet Gynecol 2018; 218(2s): S725-s37.
2. Damhuis SE, Ganzevoort W, Gordijn SJ. Abnormal Fetal Growth: Small for Gestational Age, Fetal Growth Restriction, Large for Gestational Age: Definitions and Epidemiology. Obstet Gynecol Clin North Am 2021; 48(2): 267-279.
3. Melamed N, Baschat A, Yinon Y, 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(Suppl 1): 3-57.
4. Zhang J, Merialdi M, Platt LD, Kramer MS. Defining normal and abnormal fetal growth: promises and challenges. Am J Obstet Gynecol 2010; 202(6): 522-528.
5. de Onis M, Blössner M, Villar J. Levels and patterns of intrauterine growth retardation in developing countries. Eur J Clin Nutr 1998; 52(Suppl 1): S5-15.
6. Vicoveanu P, Vasilache IA, Nemescu D, et al. Predictors Associated with Adverse Pregnancy Outcomes in a Cohort of Women with Systematic Lupus Erythematosus from Romania-An Observational Study (Stage 2). J Clin Med 2022; 11(7): 1964.
7. Harabor V, Mogos R, Nechita A, et al. Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity. International Journal of Environmental Research and Public Health 2023; 20(3): 2380.
8. Mustafa HJ, Javinani A, Heydari MH, Saldaña AV, Rohita DK, Khalil A. Selective intrauterine growth restriction without concomitant twin-to-twin transfusion syndrome, natural history, and risk factors for fetal death: A systematic review and meta-analysis. Am J Obstet Gynecol MFM. 2023; 5(10): 101105.
9. Seravalli V, Maoloni L, Pasquini L, et al. The impact of assisted reproductive technology on prenatally diagnosed fetal growth restriction in dichorionic twin pregnancies. PLoS One. 2020; 15(4): e0231028.
10. Lees CC, Romero R, Stampalija T, et al. Clinical Opinion: The diagnosis and management of suspected fetal growth restriction: an evidence-based approach. Am J Obstet Gynecol. 2022; 226(3): 366-378.
11. Melinte-Popescu AS, Popa RF, et al. Managing Fetal Ovarian Cysts: Clinical Experience with a Rare Disorder. Medicina (Kaunas) 2023; 59(4): 715.
12. Zonda GI, Mogos R, Melinte-Popescu AS, et al. Hematologic Risk Factors for the Development of Retinopathy of Prematurity - A Retrospective Study. Children (Basel) 2023; 10(3): 567.
13. Adam AM, Popa RF, Vaduva C, et al. Pregnancy Outcomes, Immunophenotyping and Immunohistochemical Findings in a Cohort of Pregnant Patients with COVID-19-A Prospective Study. Diagnostics (Basel) 2023; 13(7): 1345.
14. Stepan H, Galindo A, Hund M, et al. Clinical utility of sFlt-1 and PlGF in screening, prediction, diagnosis and monitoring of pre-eclampsia and fetal growth restriction. Ultrasound Obstet Gynecol 2023; 61(2): 168-180.
15. Gomez-Roig MD, Mazarico E, Sabria J, Parra J, Oton L, Vela A. Use of placental growth factor and uterine artery doppler pulsatility index in pregnancies involving intrauterine fetal growth restriction or preeclampsia to predict perinatal outcomes. Gynecol Obstet Invest. 2015; 80(2): 99-105.
16. Gordijn SJ, Beune IM, Thilaganathan B, et al. Consensus definition of fetal growth restriction: a Delphi procedure. Ultrasound Obstet Gynecol 2016; 48(3): 333-339.
17. Melinte-Popescu M, Vasilache IA, Socolov D, Melinte-Popescu AS. Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study. Diagnostics (Basel) 2023; 13(2): 287.
18. Vicoveanu P, Vasilache IA, Scripcariu IS, et al. Use of a Feed-Forward Back Propagation Network for the Prediction of Small for Gestational Age Newborns in a Cohort of Pregnant Patients with Thrombophilia. Diagnostics (Basel) 2022; 12(4): 1099.
19. Rescinito R, Ratti M, Payedimarri AB, Panella M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare (Basel) 2023; 11(11): 1617.
20. Huang KH, Chen FY, Liu ZZ, et al. Prediction of pre-eclampsia complicated by fetal growth restriction and its perinatal outcome based on an artificial neural network model. Front Physiol 2022; 13: 992040.
21. Sharma N, Zahoor I, Sachdeva M, et al. Deciphering the role of nanoparticles for management of bacterial meningitis: an update on recent studies. Environ Sci Pollut Res Int 2021; 28(43): 60459-60476.
22. Herraiz I, Simón E, Gómez-Arriaga PI, et al. Angiogenesis-Related Biomarkers (sFlt-1/PLGF) in the Prediction and Diagnosis of Placental Dysfunction: An Approach for Clinical Integration. Int J Mol Sci 2015; 16(8): 19009-19026.

Additional Files

Published

2023-12-21