Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.

Journal: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
PMID: 33142359

Abstract

OBJECTIVES: To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert.

Authors

  • F Christiansen
    School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden.
  • E L Epstein
    School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden.
  • E Smedberg
    Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden.
  • M Åkerlund
    Harvard Extension School, Harvard University, Cambridge, MA, USA.
  • K Smith
    Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR 72205, USA.
  • E Epstein
    Department of Clinical Science and Education, Karolinska Institutet, and Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden.

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