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.

To develop a segmentation pipeline for segmentation of aortic dissection CT angiograms into true and false lumina on multiplanar reformations (MPRs) perpendicular to the aortic centerline and derive quantitative morphologic features, specifically aortic diameter and true-or false-lumen cross-sectional area. Materials and Methods: An automated segmentation pipeline including two convolutional neural network (CNN) segmentation algorithms was developed. The algorithm derives the aortic centerline, generates MPRs orthogonal to the centerline, and segments the true and false lumina. A total of 153 CT angiograms obtained from 45 retrospectively identified patients (mean age, 50 years; range, 22-79 years) were used to train (n = 103), validate (n = 22), and test (n = 28) the CNN pipeline. Accuracy was evaluated by using the Dice similarity coefficient (DSC). Segmentations were then used to derive the maximal diameter of test-set patients and cross-sectional area profiles of the true and false lumina. Results:The segmentation pipeline yielded a mean DSC of 0.873 6 0.056 for the true lumina and 0.894 6 0.040 for the false lumina of test-set cases. Automated maximal diameter measurements correlated well with manual measurements (R 2 = 0.95). Profiles of crosssectional diameter, true-lumen area, and false-lumen area over several follow-up examinations were derived. Conclusion:A segmentation pipeline was used to accurately identify true and false lumina on CT angiograms of aortic dissection. These segmentations can be used to obtain diameter and other morphologic parameters for surveillance and risk stratification.

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Funding Acknowledgements Type of funding sources: None. Introduction Number of Cardiac CT examinations in paediatric population is rising. Clinical questions might vary from cardiac anatomy, coronary arteries, cardiac function assessment to chest examination including evaluation of large vessels, eventually combinations of these tasks. Dose reduction in children is especially important. CT scanning mode selection, reduction of kVp and limited extent of scanning in z-axis are the most important parameters for dose reduction. However, dose reduction optimization should not limit the readability of the CCT, and consequently the ability to reply the clinical question. Purpose This study analyses the influence of different clinical questions to radiation dose of paediatric cardiac CT. Methods In total 123 patients (42 females, 81 males) were examined using third generation dual-source CT scanner (SOMATOM Force, Siemens Healthineers, Forchheim, Germany), age mean 10.5 ± SD 5.9 years (min 50 days – max 18.9 years), height 138 ± 39 cm (54 – 200), weight 49 ± 25 kg (3.7 – 103). Three main categories of clinical questions (Q) were set: Q1) Extent of scanning in z-axis: heart only (from carina to diaphragm) or whole chest, Q2) Coronary arteries evaluation: yes/no, Q3) Cardiac function assessment (yes/no). Radiation dose is represented as a dose length product (DLP) in mGy*cm. Effective dose (ED) in mSv and calculated from DLP using converting factor 0.026 mSv/mGy*cm. Multiple regression was used to evaluate influence of clinical question to radiation dose. Results Total DLP was 202 ± 282 mGy*cm (6 – 1751), ED was 5.25 ± 7.33 mSv (0.16 – 45.53). Multiple regression of Total DLP versus Q1-Q3, height, weight and average heart rate showed statistically significant influence of cardiac function assessment (413 ± 263 vs 132 ± 253 mGy*cm, p 0.0001) and weight (p 0.0443). Multiple regression after adjusting to weight showed statistically significant influence of cardiac function assessment only (p &lt;0.0001). Conclusions Only clinical question about cardiac function assessment caused statistically significant higher radiation dose acquired from cardiac CT in paediatric patients. There was no statistically significant influence of coronary artery evaluation and extent of scanning. Abstract Figure.