Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

Objectives Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. Methods Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. Results The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). Conclusions Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation. Key Points • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

Multiple sclerosis (MS) is an autoimmune, inflammatory demyelinating disease of the central nervous system characterized in the majority of the patients by a relapsing-remitting disease course. For decades high-dosage corticosteroids (CS) are considered the cornerstone in the management of acute MS relapses. However, many unanswered questions remain when it comes to the exact modalities of CS administration. In this review on behalf of the Belgian Study Group for MS we define the efficacy of CS in reducing MS-related morbidity and examine whether the effect is different according to type of CS, route of administration, cumulative dosage, timing of initiation and disease course. We also review the use of CS in combination with other MS treatments and during pregnancy and lactation. Furthermore, we delineate the relevant adverse events due to a pulse CS regimen and present a decision tree that can be used when treating MS relapses in clinical practice.

Snowboarding and skiing remain the two most popular winter sports worldwide. Musculoskeletal (MSK) injuries are common in snowboarding, and the number has increased significantly since the advent of snow parks. The number of injuries is the highest for novice snowboarders; more experienced boarders generally sustain more severe injuries. Snowboarders can experience a wide array of MSK injuries, but some injury types are more frequently encountered because of the specific injury mechanism unique to snowboarding. This article reviews the most common snowboarding injuries with a focus on the current understanding of the injury mechanism and provides an approach to imaging.

Skiing is a continuously evolving winter sport, responsible for a considerable number of musculoskeletal injuries. Specific injury patterns and mechanisms in the upper and lower extremities, head, and spine are influenced by skier expertise and skill, position during injury, and environmental conditions. Predilection for certain joints and injury patterns have changed over time, largely due to technological advancements in equipment, increased awareness campaigns, and preventive protocols. Knowledge and understanding of these trends and developments can aid the radiologist to reach a timely and accurate diagnosis, thereby guiding clinical management and potentially reducing the overall incidence of debilitation and death.

Introduction Imaging plays a crucial role in the diagnosis, prognosis and follow-up of traumatic brain injury. Whereas computed tomography plays a pivotal role in the acute setting, magnetic resonance imaging is best suited to detect the true extent of traumatic brain injury, and more specifically diffuse axonal injury. Post-traumatic brain atrophy is a well-known complication of traumatic brain injury. Purpose This study investigated the correlation between diffuse axonal injury detected with fluid-attenuated inversion recovery and susceptibility-weighted imaging magnetic resonance imaging, post-traumatic brain atrophy and functional outcome (Glasgow outcome scale – extended). Materials and methods Twenty patients with a closed head injury and diffuse axonal injury detected with fluid-attenuated inversion recovery and susceptibility-weighted imaging were included. The total volumes of the diffuse axonal injury fluid-attenuated inversion recovery lesions were determined for each subject’s initial (<14 days) and follow-up magnetic resonance scan (average: day 303 ± 83 standard deviation). The different brain volumes were automatically quantified using a validated and both US Food and Drug Administration-cleared and CE-marked machine learning algorithm (icobrain). The number of susceptibility-weighted imaging lesions and functional outcome scores (Glasgow outcome scale – extended) were retrieved from the Collaborative European NeuroTrauma Effectiveness Research Traumatic Brain Injury dataset. Results The volumetric fluid-attenuated inversion recovery diffuse axonal injury lesion load showed a significant inverse correlation with functional outcome (Glasgow outcome scale – extended) ( r = −0.57; P = 0.0094) and white matter volume change ( r = −0.50; P = 0.027). In addition, white matter volume change correlated significantly with the Glasgow outcome scale – extended score ( P = 0.0072; r = 0.58). Moreover, there was a strong inverse correlation between longitudinal fluid-attenuated inversion recovery lesion volume change and whole brain volume change ( r = −0.63; P = 0.0028). No significant correlation existed between the number of diffuse axonal injury susceptibility-weighted imaging lesions, brain atrophy and functional outcome. Conclusions Volumetric analysis of diffuse axonal injury on fluid-attenuated inversion recovery imaging and automated brain atrophy calculation are potentially useful tools in the clinical management and follow-up of traumatic brain injury patients with diffuse axonal injury.

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