Clinical imaging
-
Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled 'ground truth' data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. ⋯ One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods - particularly supervised machine learning and deep learning - with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications.
-
The coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, is gradually spreading worldwide. For diagnosis, chest computed tomography is a conventional, noninvasive imaging modality that is very accurate for detection and evaluation of pneumonia and is an important adjunct to real-time reverse transcription polymerase chain reaction diagnosis of the virus. Previous studies have reported typical computed tomography imaging features indicative of COVID-19, such as multifocal ground-glass opacities with or without consolidation. ⋯ Thus, advanced training and education in standardized infection control and prevention practice are essential. The purpose of this brief review is to summarize such training and education for clinical management of this outbreak for radiology department personnel. We will describe standard transmission-based precautions, workflow for computed tomography examination of fever patients, and decontamination management of a radiology department.
-
Social media are impacting all industries and changing the way daily interactions take place. This has been notable in health care as it allows a mechanism to connect patients directly to physicians, advocacy groups, and health care information. ⋯ Often, articles in the lay press have little medical expertise informing opinions about artificial intelligence in radiology. We propose solutions for radiologists to take the lead in the narrative on social media about the role of AI in radiology to better inform and shape public perception about the role of AI in radiology.
-
Since first report of a novel coronavirus in December of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has crippled healthcare systems around the world. While many initial screening protocols centered around laboratory detection of the virus, early testing assays were thought to be poorly sensitive in comparison to chest computed tomography, especially in asymptomatic disease. Coupled with shortages of reverse transcription polymerase chain reaction (RT-PCR) testing kits in many parts of the world, these regions instead turned to the use of advanced imaging as a first-line screening modality. ⋯ Though current national and international guidelines recommend for the use of RT-PCR as the primary screening tool for suspected cases of COVID-19, institutional and regional protocols must consider local availability of resources when issuing universal recommendations. Successful containment and social mitigation strategies worldwide have been thus far predicated on unified governmental responses, though the underlying ideologies of these practices may not be widely applicable in many Western nations. As the strain on the radiology workforce continues to mount, early results indicate a promising role for the use of machine-learning algorithms as risk stratification schema in the months to come.
-
Since the spread of the coronavirus disease 2019 (COVID-19) was designated as a pandemic by the World Health Organization, health care systems have been forced to adapt rapidly to defer less urgent care during the crisis. The United States (U. S.) has adopted a four-phase approach to decreasing and then resuming non-essential work. ⋯ Tiered systems are proposed for the prioritization of elective procedures, with physician-to-physician communication encouraged. Infection control methods, provision of personal protective equipment (PPE), and physical distancing measures are highlighted. Finally, changes in hours of operation, hiring strategies, and remote reading services are discussed for their potential to ease the transition to normal operations.