Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

Journal: Journal of computer assisted tomography
Published Date:

Abstract

Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

Authors

  • Ahmed W Moawad
    Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA.
  • David T Fuentes
    Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Mohamed G ElBanan
    Department of Diagnostic and Interventional Radiology, Yale New Haven Health, Bridgeport Hospital, CT.
  • Ahmed S Shalaby
    From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Jeffrey Guccione
    Department of Diagnostic and Interventional Imaging, The University of Texas Health Sciences Center at Houston, Houston, TX.
  • Serageldin Kamel
    Clinical Neurosciences Imaging Center, Yale University School of Medicine, New Haven, CT.
  • Corey T Jensen
    Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473, Houston, TX 77030-4009.
  • Khaled M Elsayes
    Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA.