Abstract
Induction of labor (IOL) is a very common procedure in current obstetrics; about 20% of women who undergo IOL at term pregnancy end up needing a cesarean section (C-section). The standard method to assess the risk of C-section, known as Bishop Score, is subjective and inconsistent. Thus, in this paper a novel method to predict the failure of IOL is presented, based on the analysis of B-mode transvaginal ultrasound (US) images. Advanced radiomic analyses from these images are combined with sonographic measurements (e.g. cervical length, cervical angle) and clinical data from a total of 182 patients to generate the predictive model. Different machine learning methods are compared, achieving a maximum AUC of 0.75, with 69% sensitivity and 71% specificity when using a Random Forest classifier. These preliminary results suggest that features obtained from US images can be used to estimate the risk of IOL failure, providing the practitioners with an objective method to choose the most personalized treatment for each patient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Adwy, A.M., et al.: Diagnostic accuracy of posterior cervical angle and cervical length in the prediction of successful induction of labor. Int. J. Gynecol. Obstet. 141(1), 102–107 (2018)
Baños, N., Perez-Moreno, A., Julià, C., Murillo-Bravo, C., Coronado, D., Gratacós, E., Deprest, J., Palacio, M.: Quantitative analysis of cervical texture by ultrasound in mid-pregnancy and association with spontaneous preterm birth: cervical texture associated with spontaneous preterm birth. Ultrasound Obstet. Gynecol. 51(5), 637–643 (2018)
Baños, N., et al.: Quantitative analysis of the cervical texture by ultrasound and correlation with gestational age. Fetal Diagn. Ther. 41(4), 265–272 (2017)
Brik, M., Mateos, S., Fernandez-Buhigas, I., Garbayo, P., Costa, G., Santacruz, B.: Sonographical predictive markers of failure of induction of labour in term pregnancy. J. Obstet. Gynaecol. 37(2), 179–184 (2017)
Burgos-Artizzu, X.P., Perez-Moreno, A., Coronado-Gutierrez, D., Gratacos, E., Palacio, M.: Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis. Sci. Rep. 9(1), 1950 (2019)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), 104–107 (2017)
Kehila, M., Abouda, H., Sahbi, K., Cheour, H., Chanoufi, M.B.: Ultrasound cervical length measurement in prediction of labor induction outcome. NPM 9(2), 127–131 (2016)
Kolkman, D., et al.: The bishop score as a predictor of labor induction success: a systematic review. Am. J. Perinatol. 30(08), 625–630 (2013)
Obando, V.P., Arana, A.N., Izaguirre, A., Burgos, J.: Labor induction failure prediction based on B-mode ultrasound image processing using multiscale local binary patterns. In: International Conference on Optoelectronics and Image Processing, pp. 25–29 (2016)
Obando, V.P., Arana, A.N., Izaguirre, A., Burgos, J.: Labor induction failure prediction using Gabor filterbanks and center symmetric local binary patterns. In: IEEE 37th Central America and Panama Convention, pp. 1–5 (2017)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
García Ocaña, M.I., López-Linares Román, K., Burgos San Cristóbal, J., del Campo Real, A., Macía Oliver, I. (2019). Prediction of Failure of Induction of Labor from Ultrasound Images Using Radiomic Features. In: Wang, Q., et al. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. PIPPI SUSI 2019 2019. Lecture Notes in Computer Science(), vol 11798. Springer, Cham. https://doi.org/10.1007/978-3-030-32875-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-32875-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32874-0
Online ISBN: 978-3-030-32875-7
eBook Packages: Computer ScienceComputer Science (R0)
