TY - JOUR
T1 - Artificial Intelligence in Diagnostic Radiology
T2 - Where Do We Stand, Challenges, and Opportunities
AU - Moawad, Ahmed W.
AU - Fuentes, David T.
AU - Elbanan, Mohamed G.
AU - Shalaby, Ahmed S.
AU - Guccione, Jeffrey
AU - Kamel, Serageldin
AU - Jensen, Corey T.
AU - Elsayes, Khaled M.
N1 - Publisher Copyright:
© Wolters Kluwer Health, Inc. All rights reserved.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - convolutional neural network
KW - generative adversarial networks
KW - machine learning
KW - neural networks
KW - quality control
KW - recurrent neural network
KW - workflow organization
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U2 - 10.1097/RCT.0000000000001247
DO - 10.1097/RCT.0000000000001247
M3 - Review article
C2 - 35027520
AN - SCOPUS:85123969000
SN - 0363-8715
VL - 46
SP - 78
EP - 90
JO - Journal of computer assisted tomography
JF - Journal of computer assisted tomography
IS - 1
ER -