Deep learning algorithm for predicting preterm birth in the case of threatened preterm labor admissions using transvaginal ultrasound.

Journal: Journal of medical ultrasonics (2001)
Published Date:

Abstract

PURPOSE: Preterm birth presents a major challenge in perinatal care, and predicting preterm birth remains a major challenge. If preterm birth cases can be accurately predicted during pregnancy, preventive interventions and more intensive prenatal monitoring may be possible. Deep learning has the capability to extract image parameters or features related to diseases. We constructed a deep learning model to predict preterm births using transvaginal ultrasound images.

Authors

  • Ai Ohtaka
    Department of Obstetrics and Gynecology, Tokyo Women's Medical University Adachi Medical Center, 2-1-10 Kohoku, Adachi-ku, Tokyo, Japan.
  • Munetoshi Akazawa
    Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan. Electronic address: navirez@yahoo.co.jp.
  • Kazunori Hashimoto
    Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.