A Review of Applications of Machine Learning in Mammography and Future Challenges.

Journal: Oncology
Published Date: May 21, 2021

Abstract

BACKGROUND: The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were searched for relevant literature. Studies evaluating AI models in the context of prediction and diagnosis of breast malignancies that also reported conventional performance metrics were deemed suitable for inclusion. From 90 unique citations, 21 studies were considered suitable for our examination. Data was not pooled due to heterogeneity in study evaluation methods.

Authors

  • Sai Batchu
    National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA, batchus1@tcnj.edu.
  • Fan Liu
    Hunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
  • Ahmad Amireh
    Duke University Medical Center, Durham, North Carolina, USA.
  • Joseph Waller
    Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
  • Muhammad Umair
    Department of Radiology, Johns Hopkins University, Baltimore, Maryland, United States.

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