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2.
Semin Musculoskelet Radiol ; 26(1): 41-53, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-35139558

RESUMO

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.


Assuntos
Traumatismos em Atletas , Esqui , Traumatismos em Atletas/diagnóstico por imagem , Humanos , Incidência , Extremidade Inferior/lesões , Fatores de Risco
3.
Semin Musculoskelet Radiol ; 26(1): 54-68, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-35139559

RESUMO

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.


Assuntos
Traumatismos em Atletas , Esqui , Traumatismos em Atletas/diagnóstico por imagem , Traumatismos em Atletas/epidemiologia , Humanos
4.
Skeletal Radiol ; 51(9): 1889-1897, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-35169938

RESUMO

We describe a case of late-onset sciatic neuralgia due to cicatricial tethering of the sciatic nerve by a retracted torn hamstring muscle that was successfully treated with percutaneous neurolysis. Ultrasound and MRI showed a chronic complete avulsion of the proximal hamstring complex with fatty atrophy of the retracted hamstring muscles. Dynamic ultrasound and magnetic resonance imaging displayed tethering of the retracted hamstring complex to the sciatic nerve caused by cicatricial adhesions. Whereas hamstring injuries are highly prevalent sports injuries, there are only a small number of reported cases in the literature of late-onset sciatic nerve involvement. We highlight the benefits of dynamic ultrasound and magnetic resonance imaging and propose ultrasound-guided percutaneous neurolysis as a viable minimally invasive treatment option.


Assuntos
Traumatismos em Atletas , Músculos Isquiossurais , Síndromes de Compressão Nervosa , Traumatismos dos Nervos Periféricos , Traumatismos em Atletas/cirurgia , Humanos , Imageamento por Ressonância Magnética/métodos , Nervo Isquiático/diagnóstico por imagem , Nervo Isquiático/cirurgia
5.
Neuroradiol J ; 35(4): 468-476, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34643120

RESUMO

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.


Assuntos
Lesões Encefálicas Traumáticas , Lesão Axonal Difusa , Atrofia , Humanos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética
6.
Eur Radiol ; 31(11): 8797-8806, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33974148

RESUMO

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.


Assuntos
Inteligência Artificial , Radiologia , Humanos , Motivação , Radiologistas , Inquéritos e Questionários
7.
Eur Radiol ; 31(9): 7058-7066, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33744991

RESUMO

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.


Assuntos
Inteligência Artificial , Radiologia , Adulto , Medo , Humanos , Masculino , Radiologistas , Inquéritos e Questionários
8.
J Belg Soc Radiol ; 103(1): 35, 2019 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-31149652

RESUMO

INTRODUCTION: Belgium counts 1,888 active radiologists. This is an average of 16.2 radiologists per 100,000 people, which is slightly more than the European average of 12.7 per 100,000. Feedback from recently graduated residents suggests difficulties in finding a permanent staff member position and a high demand for dedicated profiles in radiology departments. To objectify this, the Young Radiologist Section (YRS) of the Belgian Society of Radiology (BSR) performed a survey of the radiology job market in Belgium. MATERIAL AND METHODS: An anonymous survey was sent to recently graduated Belgian radiologists (2013-2018) and to the heads of all Belgian radiology departments. RESULTS: The majority of the responding graduates found a permanent staff member position as a radiologist within two years after graduation and around half of the respondents even before graduation (50% in the graduates 2018 and 57% in graduates of 2013-2017). However, a small portion of the responding graduates (8%) needed more than two years to find a staff member position.Of the responding departments, 44% prefers to appoint a radiologist with extra training in one or more subspecialties. The top three of most desired subspecialties is: musculoskeletal imaging, interventional radiology and breast imaging. CONCLUSION: Half of the responding graduates did not find a permanent staff member position before graduation. However, >90% found such a position within the first two years after graduation. There is a demand for dedicated profiles in almost half of the radiology departments.

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