Japanese journal of radiology
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The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.
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Improvements in positron emission tomography (PET) technology have contributed to increased diagnostic accuracy in patients with large-vessel vasculitis (LVV) over the last decades. Many systematic reviews and meta-analyses were conducted, and earlier diagnosis by 18F-FDG PET can be made in patients suspected of having LVV. ⋯ In most patients, disease activity cannot be monitored by laboratory tests alone; therefore, glucose metabolism may be a source for possible biomarkers. In this review, we present current concepts regarding 18F-FDG PET/CT imaging standards.
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Review
Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review.
Available information on chest Computed Tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) is constantly evolving. Ground glass opacities and consolidation with bilateral and peripheral distribution were reported as the most common CT findings, but also less typical features could be identified. All radiologists should be aware of the imaging spectrum of the COVID-19 pneumonia and imaging changes in the course of the disease. Our aim is to display the chest CT findings at first assessment and follow-up through a pictorial essay, to help in the recognition of these features for an accurate diagnosis.
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Review
CT-based mediastinal compartment classifications and differential diagnosis of mediastinal tumors.
Division of the mediastinum into compartments is used to help narrow down the differential diagnosis of mediastinal tumors, assess tumor growth, and plan biopsies and surgical procedures. There are several traditional mediastinal compartment classification systems based upon anatomical landmarks and lateral chest radiograph. ⋯ These CT-based classification systems are useful for more consistent and exact diagnosis of mediastinal tumors. In this article, we review these CT-based mediastinal compartment classifications in relation to the differential diagnosis of mediastinal tumors.
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Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near future. However, various attempts and new methods of deep learning have been proposed in recent years, and their progress is extremely fast. Therefore, at the initial stage when medical artificial intelligence papers were published, the artificial intelligence technology itself may be old technology or well-known general-purpose common technology. Therefore, the author has reviewed state-of-the-art computer vision papers and presentations of 2018 using deep learning technologies, which will have future clinical potentials selected from the point of view of a radiologist such as generative adversarial network, knowledge distillation, and general image data sets for supervised learning.