Clinical radiology
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Coronavirus disease 2019 (COVID-19) has spread fast and extensively around the world, with significant mortality and morbidity. As this is a respiratory infection, chest radiography and computed tomography (CT) are important imaging techniques in the work-up of this disease. Given its highly infectious nature, cross-infection within the healthcare setting and radiology departments needs to be addressed actively and prevented. We describe the response of radiology departments in Singapore to this pandemic, in terms of diagnosis, re-configuration of the department, re-organisation and segregation of staff, infection control, managerial, and leadership issues.
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To report the epidemiological, clinical, and radiological characteristics of patients with COVID-19 in Xiaogan, Hubei, China. ⋯ Spiral CT is a sensitive examination method, which can be applied to make an early diagnosis and for evaluation of progression, with a diagnostic sensitivity and accuracy better than that of nucleic acid detection.
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The incidence of cerebral venous thrombosis (CVT) is increasing due to the widespread use of computed tomography (CT) and magnetic resonance imaging (MRI) for investigating patients with acute headaches and new onset of seizures. Alternatively referred to as cerebral venous sinus thrombosis (CVST) or dural venous sinus thrombosis (DVST), the terms encompass a broad spectrum of neurological pathologies. These include dural venous sinus thrombosis, cortical vein thrombosis, cavernous sinus thrombosis, venous infarction/haemorrhage, and the rare sequelae of intracranial hypertension or dural arteriovenous fistula. ⋯ MRI or MRI venography (MRV) are powerful techniques, provided the radiologist is aware of critical diagnostic pitfalls. In selected cases, cerebral digital subtraction angiography (DSA) can facilitate both diagnosis and anticoagulant/transcatheter thrombolytic therapy improving clinical outcome. This article will outline the condition, highlighting cerebral venous anatomy, diagnostic techniques, and pitfalls pertinent to all practising radiologists.
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Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. ⋯ The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.