Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Moawad, Ahmed W.; Fuentes, David T.; ElBanan, Mohamed G.; Shalaby, Ahmed S.; Guccione, Jeffrey; Kamel, Serageldin; Jensen, Corey T.; Elsayes, Khaled M.
• Is Part Of: Journal of computer assisted tomography. Volume 46:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: artificial intelligence -- machine learning -- neural networks -- convolutional neural network -- recurrent neural network -- generative adversarial networks -- quality control -- workflow organization; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001247
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : 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.