Structured reporting for efficient epidemiological and in-hospital prevalence analysis of pulmonary embolisms. / Strukturierte Befundung zur effizienten Erhebung von epidemiologischen Daten und Analyse der innerklinischen Prävalenz bei Lungenarterienembolien.

Structured reporting (SR) not only offers advantages regarding report quality but, as an IT-based method, also the opportunity to aggregate and analyze large, highly structured datasets (data mining). In this study, a data mining algorithm was used to calculate epidemiological data and in-hospital prevalence statistics of pulmonary embolism (PE) by analyzing structured CT reports.All structured reports for PE CT scans from the last 5 years (n = 2790) were extracted from the SR database and analyzed. The prevalence of PE was calculated for the entire cohort and stratified by referral type and clinical referrer. Distributions of the manifestation of PEs (central, lobar, segmental, subsegmental, as well as left-sided, right-sided, bilateral) were calculated, and the occurrence of right heart strain was correlated with the manifestation.The prevalence of PE in the entire cohort was 24% (n = 678). The median age of PE patients was 71 years (IQR 58-80), and the sex distribution was 1.2/1 (M/F). Outpatients showed a lower prevalence of 23% compared to patients from regular wards (27%) and intensive care units (30%). Surgically referred patients had a higher prevalence than patients from internal medicine (34% vs. 22%). Patients with central and bilateral PEs had a significantly higher occurrence of right heart strain compared to patients with peripheral and unilateral embolisms.Data mining of structured reports is a simple method for obtaining prevalence statistics, epidemiological data, and the distribution of disease characteristics, as demonstrated by the PE use case. The generated data can be helpful for multiple purposes, such as for internal clinical quality assurance and scientific analyses. To benefit from this, consistent use of SR is required and is therefore recommended. · SR-based data mining allows simple epidemiologic analyses for PE.. · The prevalence of PE differs between outpatients and inpatients.. · Central and bilateral PEs have an increased risk of right heart strain.. · Jorg T, Halfmann MC, Graafen D et al. Structured reporting for efficient epidemiological and in-hospital prevalence analysis of pulmonary embolisms. Fortschr Röntgenstr 2024; DOI 10.1055/a-2301-3349.

A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports.

OBJECTIVES: Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. METHODS: Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. RESULTS: Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). CONCLUSION: The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. CRITICAL RELEVANCE STATEMENT: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. KEY POINTS: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

PURPOSE: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans. This study aimed at establishing an automated estimation of kidney volume in non-contrast, low-dose CT scans of patients with suspected urolithiasis. METHODS: The kidney segmentation process was automated with 2D Convolutional Neural Network (CNN) models trained on manually segmented 2D transverse images extracted from low-dose, unenhanced CT scans of 210 patients. The models' segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), for the overlap with manually-generated masks on a set of images not used in the training. Next, the models were applied to 22 previously unseen cases to segment kidney regions. The volume of each kidney was calculated from the product of voxel number and their volume in each segmented mask. Kidney volume results were then validated against results semi-automatically obtained by radiologists. RESULTS: The CNN-enabled kidney volume estimation took a mean of 32 s for both kidneys in a CT scan with an average of 1026 slices. The DSC was 0.91 and 0.86 and for left and right kidneys, respectively. Inter-rater variability had consistencies of ICC = 0.89 (right), 0.92 (left), and absolute agreements of ICC = 0.89 (right), 0.93 (left) between the CNN-enabled and semi-automated volume estimations. CONCLUSION: In our work, we demonstrated that CNN-enabled kidney volume estimation is feasible and highly reproducible in low-dose, non-enhanced CT scans. Automatic segmentation can thereby quantitatively enhance radiological reports.

Natural language processing for automatic evaluation of free-text answers - a feasibility study based on the European Diploma in Radiology examination.

BACKGROUND: Written medical examinations consist of multiple-choice questions and/or free-text answers. The latter require manual evaluation and rating, which is time-consuming and potentially error-prone. We tested whether natural language processing (NLP) can be used to automatically analyze free-text answers to support the review process. METHODS: The European Board of Radiology of the European Society of Radiology provided representative datasets comprising sample questions, answer keys, participant answers, and reviewer markings from European Diploma in Radiology examinations. Three free-text questions with the highest number of corresponding answers were selected: Questions 1 and 2 were "unstructured" and required a typical free-text answer whereas question 3 was "structured" and offered a selection of predefined wordings/phrases for participants to use in their free-text answer. The NLP engine was designed using word lists, rule-based synonyms, and decision tree learning based on the answer keys and its performance tested against the gold standard of reviewer markings. RESULTS: After implementing the NLP approach in Python, F1 scores were calculated as a measure of NLP performance: 0.26 (unstructured question 1, n = 96), 0.33 (unstructured question 2, n = 327), and 0.5 (more structured question, n = 111). The respective precision/recall values were 0.26/0.27, 0.4/0.32, and 0.62/0.55. CONCLUSION: This study showed the successful design of an NLP-based approach for automatic evaluation of free-text answers in the EDiR examination. Thus, as a future field of application, NLP could work as a decision-support system for reviewers and support the design of examinations being adjusted to the requirements of an automated, NLP-based review process. CLINICAL RELEVANCE STATEMENT: Natural language processing can be successfully used to automatically evaluate free-text answers, performing better with more structured question-answer formats. Furthermore, this study provides a baseline for further work applying, e.g., more elaborated NLP approaches/large language models. KEY POINTS: • Free-text answers require manual evaluation, which is time-consuming and potentially error-prone. • We developed a simple NLP-based approach - requiring only minimal effort/modeling - to automatically analyze and mark free-text answers. • Our NLP engine has the potential to support the manual evaluation process. • NLP performance is better on a more structured question-answer format.

Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case.

PURPOSE: To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports. METHODS: The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department's structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated. RESULTS: The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36-62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy. CONCLUSION: SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt.

Implementation of structured reporting in clinical routine: a review of 7 years of institutional experience.

BACKGROUND: To evaluate the implementation process of structured reporting (SR) in a tertiary care institution over a period of 7 years. METHODS: We analysed the content of our image database from January 2016 to December 2022 and compared the numbers of structured reports and free-text reports. For the ten most common SR templates, usage proportions were calculated on a quarterly basis. Annual modality-specific SR usage was calculated for ultrasound, CT, and MRI. During the implementation process, we surveyed radiologists and clinical referring physicians concerning their views on reporting in radiology. RESULTS: As of December 2022, our reporting platform contained more than 22,000 structured reports. Use of the ten most common SR templates increased markedly since their implementation, leading to a mean SR usage of 77% in Q4 2022. The highest percentages of SR usage were shown for trauma CT, focussed assessment with ultrasound for trauma (FAST), and prostate MRI: 97%, 95%, and 92%, respectively, in 2022. Overall modality-specific SR usage was 17% for ultrasound, 13% for CT, and 6% for MRI in 2022. Both radiologists and referring physicians were more satisfied with structured reports and rated SR better than free-text reporting (FTR) on various attributes. CONCLUSIONS: The increasing SR usage during the period under review and the positive attitude towards SR among both radiologists and clinical referrers show that SR can be successfully implemented. We therefore encourage others to take this step in order to benefit from the advantages of SR. KEY POINTS: 1. Structured reporting usage increased markedly since its implementation at our institution in 2016. 2. Mean usage for the ten most popular structured reporting templates was 77% in 2022. 3. Both radiologists and referring physicians preferred structured reports over free-text reports. 4. Our data shows that structured reporting can be successfully implemented. 5. We strongly encourage others to implement structured reporting at their institutions.

Pediatric computed tomography doses in Germany from 2016 to 2018 based on large-scale data collection.

PURPOSE: Accumulating evidence from epidemiological studies that pediatric computed tomography (CT) examinations can be associated with a small but non-zero excess risk for developing leukemia or brain tumor highlights the need to optimize doses of pediatric CT procedures. Mandatory dose reference levels (DRL) can support reduction of collective dose from CT imaging. Regular surveys of applied dose-related parameters are instrumental to decide when technological advances and optimized protocol design allow lower doses without sacrificing image quality. Our aim was to collect dosimetric data to support adapting current DRL to changing clinical practice. METHOD: Dosimetric data and technical scan parameters from common pediatric CT examinations were retrospectively collected directly from Picture Archiving and Communication Systems (PACS), Dose Management Systems (DMS), and Radiological Information Systems (RIS). RESULTS: We collected data from 17 institutions on 7746 CT series from the years 2016 to 2018 from examinations of the head, thorax, abdomen, cervical spine, temporal bone, paranasal sinuses and knee in patients below 18 years of age. Most of the age-stratified parameter distributions were lower than distributions from previously-analyzed data from before 2010. Most of the third quartiles were lower than German DRL at the time of the survey. CONCLUSIONS: Directly interfacing PACS, DMS, and RIS installations allows large-scale data collection but relies on high data-quality at the documentation stage. Data should be validated by expert knowledge or guided questionnaires. Observed clinical practice in pediatric CT imaging suggests lowering some DRL in Germany is reasonable.

Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing.

BACKGROUND: Structured reporting (SR) is recommended in radiology, due to its advantages over free-text reporting (FTR). However, SR use is hindered by insufficient integration of speech recognition, which is well accepted among radiologists and commonly used for unstructured FTR. SR templates must be laboriously completed using a mouse and keyboard, which may explain why SR use remains limited in clinical routine, despite its advantages. Artificial intelligence and related fields, like natural language processing (NLP), offer enormous possibilities to facilitate the imaging workflow. Here, we aimed to use the potential of NLP to combine the advantages of SR and speech recognition. RESULTS: We developed a reporting tool that uses NLP to automatically convert dictated free text into a structured report. The tool comprises a task-oriented dialogue system, which assists the radiologist by sending visual feedback if relevant findings are missed. The system was developed on top of several NLP components and speech recognition. It extracts structured content from dictated free text and uses it to complete an SR template in RadLex terms, which is displayed in its user interface. The tool was evaluated for reporting of urolithiasis CTs, as a use case. It was tested using fictitious text samples about urolithiasis, and 50 original reports of CTs from patients with urolithiasis. The NLP recognition worked well for both, with an F1 score of 0.98 (precision: 0.99; recall: 0.96) for the test with fictitious samples and an F1 score of 0.90 (precision: 0.96; recall: 0.83) for the test with original reports. CONCLUSION: Due to its unique ability to integrate speech into SR, this novel tool could represent a major contribution to the future of reporting.

[Interdisciplinary case discussions]. / Interdisziplinäre Besprechungen.

BACKGROUND: Interdisciplinary case discussions, especially tumor conferences, represent a large part of the clinical radiologist's daily work. Radiology plays a key role in tumor conferences, since imaging findings have a direct influence on therapy decisions. METHODS AND OBJECTIVES: This article discusses the requirements for the radiologist in preparing and conducting tumor conferences. Furthermore, the general conditions and forms of implementation of tumor conferences will be highlighted. Information technology (IT) tools for process automation and systems for assessing the course of tumor diseases will be presented. RESULTS: Detailed preparation of tumor conferences and clear communication of findings is essential. The radiological expertise in tumor conferences often leads to changes or adjustments of initially planned therapies. In addition to traditional face-to-face meetings, hybrid solutions have become established for tumor conferences in which the core team is on site and other participants (external referring physicians, internal participants outside the core team) are connected via video conference. Various systems have been established for assessing the course of tumor diseases. Due to its broad applicability, RECIST 1.1. is the most widely used. IT tools enable previously marked lesions to be displayed over time in a matrix view (lesion tracking). Artificial intelligence (AI) can also be used to automatically detect lesions and assess their volumes. CONCLUSION: Preparing and conducting tumor conferences is time-consuming for radiologists. IT tools can automate and thus facilitate the processes. Hybrid solutions combining face-to-face meetings and video conferences make it easier for external referring physicians to present their patients in tumor conferences.

[Image interpretation and the radiological report]. / Befundkommunikation.

BACKGROUND: The radiological report is the cornerstone of communication between radiologists and referring physicians and patients, respectively. The report is comprised of image interpretation on the one hand and communication of this interpretation on the other hand. OBJECTIVES AND METHODS: To outline different types of radiological reports (regarding content as well as structure) and their communication. To this end, current guidelines are summarized and clinical examples are presented. RESULTS: The radiological report is typically a written piece of free text prose and highly individualized regarding its quality, precision, and structure. In order to improve the understanding of the written report, additional material (e.g., annotations, images, tables) can be supplemented (multimedia-enhanced reporting). In terms of standardization, national and international radiological associations promote structured reporting in radiology. However, this is not without issues. CONCLUSION: Effective communication should improve patient care and it should be clear and provided in a timely manner. As communication in clinical reality is often hampered by various factors, internal standard operating procedures (SOPs) should be developed to improve communication workflows. to improve communication procedures.

[Validation and implementation of artificial intelligence in radiology : Quo vadis in 2022?] / Validierung und Implementierung von künstlicher Intelligenz in der radiologischen Versorgung : Quo vadis im Jahr 2022?

BACKGROUND: The hype around artificial intelligence (AI) in radiology continues and the number of approved AI tools is growing steadily. Despite the great potential, integration into clinical routine in radiology remains limited. In addition, the large number of individual applications poses a challenge for clinical routine, as individual applications have to be selected for different questions and organ systems, which increases the complexity and time required. OBJECTIVES: This review will discuss the current status of validation and implementation of AI tools in clinical routine, and identify possible approaches for an improved assessment of the generalizability of results of AI tools. MATERIALS AND METHODS: A literature search in various literature and product databases as well as publications, position papers, and reports from various stakeholders was conducted for this review. RESULTS: Scientific evidence and independent validation studies are available for only a few commercial AI tools and the generalizability of the results often remains questionable. CONCLUSIONS: One challenge is the multitude of offerings for individual, specific application areas by a large number of manufacturers, making integration into the existing site-specific IT infrastructure more difficult. Furthermore, remuneration for the use of AI tools in clinical routine by health insurance companies in Germany is lacking. But in order for reimbursement to be granted, the clinical utility of new applications must first be proven. Such proof, however, is lacking for most applications.

Dose Reduction and Image Quality in Photon-counting Detector High-resolution Computed Tomography of the Chest: Routine Clinical Data.

PURPOSE: Photon-counting detector computed tomography (PCD-CT) has the potential to significantly improve CT imaging in many ways including, but not limited to, low-dose high-resolution CT (HRCT) of the lung. The aim of this study was to perform an intrapatient comparison of the radiation dose and image quality of PCD-CT compared with conventional energy-integrating detector CT (EID-CT). METHODS: A total of 32 consecutive patients with available PCD-CT and EID-CT HRCT scans were included in the final analysis. The CT dose index (CTDI vol ) was extracted from patient dose reports. Qualitative image analysis comprised the lung parenchyma and mediastinal structures and was assessed by 3 readers using a 5-point Likert scale. Quantitative image analysis included assessment of noise and signal-to-noise ratio in the lung parenchyma, trachea, aorta, muscle, and background. RESULTS: The mean CTDI vol was 2.0 times higher in the conventional EID-CT scans (1.8±0.5 mGy) compared with PCD-CT (0.9±0.5 mGy, P <0.001). The overall image quality was rated significantly better by all 3 raters ( P <0.001) in the PCD-CT relative to the EID-CT. Quantitative analysis showed no significant differences in noise and signal-to-noise ratio in the lung parenchyma between PCD-CT and EID-CT. CONCLUSION: Compared with conventional EID-CT scans, PCD-CT demonstrated similar or better objective and subjective image quality at significantly reduced dose levels in an intrapatient comparison. These results and their effect on clinical decision-making should be further investigated in prospective studies.

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

OBJECTIVES: In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. METHODS: Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). RESULTS: Sensitivity and specificity ranges were 62-96% and 31-80%, respectively. Negative and positive predictive values ranged between 82-99% and 19-25%, respectively. AUC was in the range 0.54-0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54-0.69. CONCLUSIONS: This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. KEY POINTS: • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.

Multimodal Deep Learning for Prognosis Prediction in Renal Cancer.

BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients. OBJECTIVE: In the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC. DESIGN SETTING AND PARTICIPANTS: Two mixed cohorts of non-metastatic and metastatic ccRCC patients were used: (1) The Cancer Genome Atlas cohort including 230 patients and (2) the Mainz cohort including 18 patients with ccRCC. For each of these patients, we trained the MMDLM on multiscale histopathological images, CT/MRI scans, and genomic data from whole exome sequencing. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcome measurements included Harrell's concordance index (C-index) and also various performance parameters for predicting the 5-year survival status (5YSS). Different visualization techniques were used to make our model more transparent. RESULTS: The MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. Training on a combination of data from different sources yielded significantly better results compared to when only one source was used. Furthermore, the MMDLM's prediction was an independent prognostic factor outperforming other clinical parameters. INTERPRETATION: Multimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease. PATIENT SUMMARY: An AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer.

Online teaching in radiology as a pilot model for modernizing medical education: results of an international study in cooperation with the ESR.

BACKGROUND: Due to the outbreak of the coronavirus disease 2019 (COVID-19), it proved necessary to rapidly change medical education from on-site to online teaching. Thus, medical educators were forced to rethink the purpose of teaching and the best form of transmission of knowledge. In cooperation with the European Society of Radiology (ESR), we investigated the attitudes of radiologists in Europe and North America toward innovative online teaching concepts. METHODS: In total, 224 radiologists from 31 different countries participated in our cross-sectional, web-based survey study. On a 7-point Likert scale, participants had to answer 27 questions about the online teaching situation before/during the pandemic, technical and social aspects of online teaching and the future role of online teaching in radiology. RESULTS: An overwhelming majority stated that radiology is particularly well-suited for online teaching (91%), that online teaching should play a more prominent role after the pandemic (73%) and that lecturers should be familiar with online teaching techniques (89%). Difficulties include a higher workload in preparing online courses (59%), issues with motivating students to follow online courses (56%) and the risk of social isolation (71%). Before the pandemic, only 12% of teaching was provided online; for the future, our participants deemed a proportion of approximately 50% online teaching appropriate. CONCLUSION: Our participants are open-minded about online teaching in radiology. As the best way of transferring knowledge in medical education is still unclear, online teaching offers potential for innovation in radiology education. To support online teaching development, a structured, framework-based "online curriculum" should be established.

Structured reporting of CT scans of patients with trauma leads to faster, more detailed diagnoses: An experimental study.

PURPOSE: This study aimed to determine whether structured reports (SRs) reduce reporting time and/or increase the level of detail for trauma CT scans compared to free-text reports (FTRs). METHOD: Eight radiology residents used SRs and FTRs to describe 14 whole-body CT scans of patients with polytrauma in a simulated emergency room setting. Each resident created both a brief report and a detailed report for each case using one of the two formats. We measured the time to complete the detailed reports and established a scoring system to objectively measure report completeness and the level of detail. Scoring sheets divided the CT findings into main and secondary criteria. Finally, the radiological residents completed a questionnaire on their opinions of the SRs and FTRs. RESULTS: The detailed SRs were completed significantly faster than the detailed FTRs (mean 19 min vs. 25 min; p < 0.001). The maximum allowance of 25 min was used for 25% of SRs and 59% of FTRs. For brief reports, the SRs contained more secondary criteria than the FTRs (p = 0.001), but no significant differences were detected in main criteria. Study participants rated their own SRs as significantly more time-efficient, concise, and clearly structured compared to the FTRs. However, SRs and FTRs were rated similarly for quality, accuracy, and completeness. CONCLUSION: We found that SRs for whole-body trauma CT add clinical value compared to FTRs because SRs reduce reporting time and increase the level of detail for trauma CT scans.

International survey on residency programs in radiology: similarities and differences among 17 countries.

OBJECTIVE: With the initiative of the ACR International Economics Committee, a multinational survey was conducted to evaluate radiology residency programs around the world. METHODS: A 31-question survey was developed. It included: economic issues, program size and length, resident's activities during daytime and call, academic aspects including syllabus and examinations. Data was tabulated using the forementioned thematic framework and was qualitatively analyzed. RESULTS: Responses were received from all 17 countries that were invited to participate (France, Netherlands, Israel, UK, Russia, USA, Japan, India, Germany, Canada, Turkey, Croatia, Serbia, Italy, Ireland, Hungary, and Greece). Residency length varied between 2 and 5 years. The certificate of residency completion is provided by a local hospital [4/17 (23%)], University [6/17 (36%)], National Board [6/17 (36%)], and Ministry of Health [1/17 (6%)]. There was variability among the number of residency programs and residents per program ranging from 15 to 300 programs per nation with a 1-700 residents in each one respectively. Salaries varied significantly and ranged from 8000 to 75,000 USD equivalent. Exams are an integral part of training in all surveyed countries. Length of call varied between 5 and 26 h and the number of monthly calls ranged from 3 to 6. The future of radiology was judged as growing in [12/17 (70%)] countries and stagnant in [5/17 (30%)] countries. DISCUSSION: Radiology residency programs worldwide have many similarities. The differences are in the structure of the residency programs. Stagnation and uncertainties need to be addressed to ensure the continued development of the next generation of radiologists. SUMMARY STATEMENT: There are many similarities in the academic aims and approach to education and training of radiology residency programs worldwide. The differences are in the structure of the residency programs and payments to individual residents.

Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry.

OBJECTIVES: We investigated the attitudes of radiologists, information technology (IT) specialists, and industry representatives on artificial intelligence (AI) and its future impact on radiological work. MATERIALS AND METHODS: During a national meeting for AI, eHealth, and IT infrastructure in 2019, we conducted a survey to obtain participants' attitudes. A total of 123 participants completed 28 items exploring AI usage in medicine. The Kruskal-Wallis test was used to identify differences between radiologists, IT specialists, and industry representatives. RESULTS: The strongest agreement between all respondents occurred with the following: plausibility checks are important to understand the decisions of the AI (93% agreement), validation of AI algorithms is mandatory (91%), and medicine becomes more efficient in the age of AI (86%). In contrast, only 25% of the respondents had confidence in the AI results, and only 17% believed that medicine will become more human through the use of AI. The answers were significantly different between the three professions for four items: relevance for protocol selection in cross-sectional imaging (p = 0.034), medical societies should be involved in validation (p = 0.028), patients should be informed about the use of AI (p = 0.047), and AI should be part of medical education (p = 0.026). CONCLUSION: Currently, a discrepancy exists between high expectations for the future role of AI and low confidence in the results. This attitude was similar across all three groups. The demand for plausibility checks and the need to prove the usefulness in randomized controlled studies indicate what is needed in future research.

Radiation dose management systems-requirements and recommendations for users from the ESR EuroSafe Imaging initiative.

The European Directive 2013/59/Euratom requires member states of the European Union to ensure justification and optimisation of radiological procedures and store information on patient exposure for analysis and quality assurance. The EuroSafe Imaging campaign of the European Society of Radiology created a working group (WG) on "Dose Management" with the aim to provide European recommendations on the implementation of dose management systems (DMS) in clinical practice. The WG follows Action 4: "Promote dose management systems to establish local, national, and European diagnostic reference levels (DRL)" of the EuroSafe Imaging Call for Action 2018. DMS are designed for medical practitioners, radiographers, medical physics experts (MPE) and other health professionals involved in imaging to support their tasks and duties of radiation protection in accordance with local and national requirements. The WG analysed requirements and critical points when installing a DMS and classified the individual functions at different performance levels. KEY POINTS: • DMS are very helpful software tools for monitoring patient exposure, optimisation, compliance with DRLs and quality assurance. • DMS can help to fulfil dosimetric aspects of the European Directive 2013/59/Euratom. • The EuroSafe WG analyses DMS requirements and gives recommendations for users.