Prediction of Failure of Induction of Labor from Ultrasound Images Using Radiomic Features

  • María Inmaculada García OcañaEmail author
  • Karen López-Linares Román
  • Jorge Burgos San Cristóbal
  • Ana del Campo Real
  • Iván Macía Oliver
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11798)


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.


Radiomics Ultrasound Induction of labor Machine learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • María Inmaculada García Ocaña
    • 1
    • 2
    Email author
  • Karen López-Linares Román
    • 1
    • 2
  • Jorge Burgos San Cristóbal
    • 3
  • Ana del Campo Real
    • 3
  • Iván Macía Oliver
    • 1
    • 2
  1. 1.VicomtechSan SebastiánSpain
  2. 2.Biodonostia Health Research InstituteSan SebastiánSpain
  3. 3.Obstetrics and Gynecology Service, Biocruces Bizkaia Health Research Institute, Cruces University Hospital, Osakidetza, UPV/EHUBaracaldoSpain

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