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Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification...

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Published in:European Radiology Vol. 31; no. 7; pp. 4872 - 4886
Main Authors: Ou, William C., Polat, Dogan, Dogan, Basak E.
Format: review Journal Article
Published: Springer Nature Jul2021
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: Deep learning in breast radiology: current progress and future directions.
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        au:
          Ou, William C.
          Polat, Dogan
          Dogan, Basak E.
        affil: Department of Radiology, Seay Biomedical Building, University of Texas Southwestern Medical Center, 2201 Inwood Road, 75390, Dallas, TX, USA
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        subj:
          Specialties, Medical
          Breast
          Artificial Intelligence
          Prospective Studies
          Scales
      ab: This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification of breast lesions, but also into areas such as risk estimation and prediction of tumor responses to therapy. Remaining challenges include limited availability of high-quality data with expert annotations and ground truth determinations, the need for further validation of initial results, and unresolved medicolegal considerations. KEY POINTS: • Deep learning (DL) continues to push the boundaries of what can be accomplished by artificial intelligence (AI) in breast imaging with distinct advantages over conventional computer-aided detection. • DL-based AI has the potential to augment the capabilities of breast radiologists by improving diagnostic accuracy, increasing efficiency, and supporting clinical decision-making through prediction of prognosis and therapeutic response. • Remaining challenges to DL implementation include a paucity of prospective data on DL utilization and yet unresolved medicolegal questions regarding increasing AI utilization.
      pubtype: Academic Journal
      doctype:
        review
        Journal Article
      ougenre: Article
    language: English
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