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.

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Purpose There is no study on the role of three-dimensional compressed sensing time of flight MR angiography (3D-CS-TOF) in the management of the WEB device. We evaluated the efficacy of 3-tesla 3D-CS-TOF for the management and follow-up of the WEB device implantations. Materials and methods Seventy-three aneurysms of 69 patients treated with the WEB device were retrospectively examined. Morphological parameters and embolization results of the aneurysms were assessed and compared on 3D-CS-TOF, CTA, and DSA images. Results Occluded, neck remnant, and recurrent aneurysms were observed in 61 (83.6%), 7 (9.6%), and 5 (6.8%) aneurysms, respectively. Inter- and intra-reader agreement values related to aneurysm size measurements were perfect. Aneurysms size, age, and proximal vessel tortuosity were negatively correlated with the visibility of the aneurysms and parent vessels on 3D-CS-TOF images (p = 0.043; p = 0.032; p < 0.001, respectively). Subarachnoid hemorrhage and age are associated with 3D-CS-TOF artifacts (p = 0.031; p = 0.005, respectively). 3D-CS-TOF findings are in perfect agreement with DSA or CT angiography (CTA) results (p < 0.001). Conclusion According to our results, 3D-CS-TOF can be an easy, fast, and reliable alternative for the management or follow-up of WEB assisted embolization.

The results show that long-term low dose ionizing radiation may lead to oxidative stress and have side-effects in antioxidant thiol groups. We may suggest supporting radiation workers by safe antioxidant nutritional formulations and following up via both physical dosimetry and biodosimetric methods.

Objectives To the best of our knowledge, there is no study on 3-Tesla (3-T) phase-contrast MRI (PC-MRI) and three-dimensional sampling perfection with application-optimized contrasts using different flip-angle evolutions (3D-SPACE-VFAM) in the evaluation of idiopathic scoliosis. This study aimed to investigate CSF abnormalities in the scoliotic spine using 3-T PC-MRI and 3D-SPACE-VFAM techniques. Methods Thirty-four patients and 14 controls were examined with spinal PC-MRI and T2-weighted 3D-SPACE-VFAM techniques. Inter-and intra-reader agreements of flow-void phenomenon on 3D-SPACE-VFAM images, and velocity values on PC-MRI data were also evaluated. Results There are statistically significant differences between scoliosis and control groups based on the highest and mean peak velocity values on PC-MRI images (p = 0.005 and p = 0.023, respectively). The main thoracic (MT) group's highest peak CSF velocity values were higher than the control group (p = 0.022). There is a significant difference between the patient and control groups regarding flow-void phenomenon scores on 3D-SPACE-VFAM images (p = 0.036). Inter-and intra-reader agreement values related to PC-MRI velocity measurements were perfect for all PC-MRI readings. Inter-and intra-reader agreement values of the flow-void phenomenon scores were moderate. Conclusions Our study has led us to conclude that idiopathic scoliosis is associated with CSF flow disturbances in parallel with the literature. MRI can demonstrate these abnormalities in a non-invasive and radiation-free way.

The aim of this study was to evaluate retrospectively the incidence and risk factors for the serious complications of pneumothorax and/or parenchymal haemorrhage occurring after computed tomography (CT) guided transthoracic biopsy. Materials and methods: The relation between the incidence of pneumothorax and parenchymal haemorrhage due to biopsy, age, sex, lesion localization, lesion size, duration of the procedure, depth of lesion, number of pleural insertions of the biopsy needle and pathology results were statistically evaluated. Results: Between 2016 and 2017, 309 cases with lesions below 3 cm in diameter of a total of 768 (40.2%) CT-guided chest biopsy patients were selected for retrospective review. The rate of pneumothorax and parenchymal haemorrhage was 18.1% (59/309) and 51% (158/309), respectively post biopsy. The number of needle pleural insertions was correlated with the development of pneumothorax (P = 0.002). At regression analysis, for parenchymal haemorrhage, lesion depth (P < 0.001) and total procedure time (p=0.036) were determined as the most important independent risk factors. Conclusion: Pneumothorax and parenchymal haemorrhage are common complications after CT-guided percutaneous biopsy. The minimum number of needle-pleural insertions, the optimal access route to the lesion and as quick as possible biopsy procedure should be selected to reduce the risk of pneumothorax and parenchymal haemorrhage.

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