Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.

Journal: Computers in biology and medicine
Published Date: Jun 14, 2024

Abstract

INTRODUCTION: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including postpartum hemorrhage. Despite its significance, PAS remains frequently underdiagnosed before delivery. This study delves into the realm of machine learning to enhance the precision of PAS classification. We introduce two distinct models for PAS classification employing ultrasound texture features.

Authors

  • Dylan Young
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.
  • Naimul Khan
  • Sebastian R Hobson
    Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Toronto Metropolitan University, Canada; Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Ontario, Canada.
  • Dafna Sussman
    Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada.

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