Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives.

Breast Imaging Reporting and Data System (BI-RADS®): a success history and particularities of its use in Brazil.

BI-RADS® is a standardization system for breast imaging reports and results created by the American College of Radiology to initially address the lack of uniformity in mammography reporting. The system consists of a lexicon of descriptors, a reporting structure with final categories and recommended management, and a structure for data collection and auditing. It is accepted worldwide by all specialties involved in the care of breast diseases. Its implementation is related to the Mammography Quality Standards Act initiative in the United States (1992) and breast cancer screening. After its initial creation in 1993, four additional editions were published in 1995, 1998, 2003 and 2013. It is adopted in several countries around the world and has been translated into 6 languages. Successful breast cancer screening programs in high-income countries can be attributed in part to the widespread use of BI-RADS®. This success led to the development of similar classification systems for other organs (e.g., lung, liver, thyroid, ovaries, colon). In 1998, the structured report model was adopted in Brazil. This article highlights the pioneering and successful role of BI-RADS®, created by ACR 30 years ago, on the eve of publishing its sixth edition, which has evolved into a comprehensive quality assurance tool for multiple imaging modalities. And, especially, it contextualizes the importance of recognizing how we are using BI-RADS® in Brazil, from its implementation to the present day, with a focus on breast cancer screening.

Diagnostic Performance of COVID-19 Reporting and Data System Classification Across Residents and Radiologists: A Retrospective Study.

OBJECTIVE: The aim of the study was to evaluate the interobserver agreement and diagnostic accuracy of COVID-19 Reporting and Data System (CO-RADS), in patients suspected COVID-19 pneumonia. METHODS: Two hundred nine nonenhanced chest computed tomography images of patients with clinically suspected COVID-19 pneumonia were included. The images were evaluated by 2 groups of observers, consisting of 2 residents-radiologists, using CO-RADS. Reverse transcriptase-polymerase chain reaction (PCR) was used as a reference standard for diagnosis in this study. Sensitivity, specificity, area under receiver operating characteristic curve (AUC), and intraobserver/interobserver agreement were calculated. RESULTS: COVID-19 Reporting and Data System was able to distinguish patients with positive PCR results from those with negative PCR results with AUC of 0.796 in the group of residents and AUC of 0.810 in the group of radiologists. There was moderate interobserver agreement between residents and radiologist with κ values of 0.54 and 0.57. CONCLUSIONS: The diagnostic performance of CO-RADS for predicting COVID-19 pneumonia showed moderate interobserver agreement between residents and radiologists.

Patient Electronic Access to Final Radiology Reports: What Is the Current Standard of Practice, and Is an Embargo Period Appropriate?

Patients have a right to their medical records, and it has become commonplace for institutions to set up online portals through which patients can access their electronic health information, including radiology reports. However, institutional approaches vary on how and when such access is provided. Many institutions have advocated built-in "embargo" periods, during which radiology reports are not immediately released to patients, to give ordering clinicians the opportunity to first receive, review, and discuss the radiology report with their patients. To understand current practices, a telephone survey was conducted of 83 hospitals identified in the 2019-2020 U.S. News & World Report Best Hospitals Rankings. Of 70 respondents, 91% (64 of 70) offered online portal access. Forty-two percent of those with online access (27 of 64 respondents) reported a delay of 4 days or longer, and 52% (33 of 64 respondents) indicated that they first send reports for review by the referring clinician before releasing to the patient. This demonstrates a lack of standardized practice in prompt patient access to health records, which may soon be mandated under the final rule of the 21st Century Cures Act. This article discusses considerations and potential benefits of early access for patients, radiologists, and primary care physicians in communicating health information and providing patient-centered care. © RSNA, 2021.

Comparison of diagnostic accuracy of digital chest X-ray images between PACS and WhatsApp Messenger in resource poor setting.

PURPOSE: Many underserved remote locations without specialists would benefit from the ability to quickly and easily share images of radiographs with trained radiologists using WhatsApp messenger. However, there is limited evidence on the role of WhatsApp messenger for sharing chest x-ray (CXR) images to aid diagnosis and management. The objective of the study was to determine the diagnostic accuracy and inter-observer agreement of WhatsApp messenger images of digital CXR compared to viewing on Picture Archiving and Communication System (PACS) monitor. METHODS: Two pulmonologists reported 400 WhatsApp messenger images of digital CXR each. After a wash period of two weeks, they reviewed the original CXR images on PACS and again reported their findings. Diagnostic agreement was measured using kappa value, diagnostic accuracy was evaluated by sensitivity and specificity. RESULTS: The diagnostic agreement between WhatsApp and PACS images for both the readers was high in case of normal CXR (0.84), Pneumonia (0.85) and Active Koch's (0.79) and Old Koch's (0.71). The inter-observer agreement between two readers on WhatsApp images was good in cases of normal chest x-ray (0.74), Active Koch's (0.61) and Pneumonia (0.74) and low in COPD (0.31) and Pleural Effusion (0.28) and Carcinoma Lung (0.40). In terms of radiological lesion, inter-observer agreement between two readers on WhatsApp images was good in terms of the zonal involvement, moderate in case of infiltrates, consolidation, nodules, and fibrosis, fair in cavity, effusion (0.28) and poor in hilar lymphadenopathy (0.14). The sensitivity in the diagnosis of nodules, effusion and hilar lymphadenopathy was <50% in both the readers. CONCLUSION: CXR transmission via WhatsApp is able to identify clinical findings similar to viewing the same image on a PACS monitor in cases of Pneumonia and normal subjects. Active and old Koch's has good comparability whereas; diagnostic agreement is poor in COPD, cavity, pleural effusion and hilar lymphadenopathy, requiring more caution during interpretation.

Validation of Diagnostic Performance and Interobserver Agreement of DTD-TIRADS for Diffuse Thyroid Disease on Ultrasound: A Single-Center Study.

OBJECTIVE. This retrospective study aimed to investigate the capability of the already-proposed thyroid imaging reporting and data system for detecting diffuse thyroid disease (DTD-TIRADS) on ultrasound (US) by assessing interobserver agreement and diagnostic performance. MATERIALS AND METHODS. A total of 180 patients who underwent thyroid US before thyroid surgery were included. Three radiologists blinded to the pathologic and serologic data independently categorized the US features according to a four-category DTD-TIRADS classification system. On the basis of the pathologic results of thyroid parenchyma, diagnostic performance values were calculated using ROC curve analyses. Interobserver agreements of each US feature and DTD-TIRADS category among the three radiologists were also assessed. RESULTS. Of the 180 patients, 143 (79.4%) had normal thyroid parenchyma and 37 (20.6%) had diffuse thyroid disease (DTD). The areas under the ROC curve for DTD were not significantly different among the three radiologists: 0.876 (95% CI, 0.819-0.920) for radiologist 1, 0.883 (95% CI, 0.827-0.926) for radiologist 2, and 0.861 (95% CI, 0.801-0.908) for radiologist 3 (p > .05). The cutoff for the diagnosis of DTD was category III DTD-TIRADS. The sensitivity, specificity, and accuracy of DTD-TIRADS for detecting DTD were 86.5%, 81.1%, and 82.2% for radiologist 1; 86.5%, 83.2%, and 83.9% for radiologist 2; and 83.8%, 82.5%, and 82.8% for radiologist 3, respectively. Interobserver agreement of DTD-TIRADS categorization was almost perfect (κ = 0.81). CONCLUSION. DTD-TIRADS has high diagnostic performance and almost-perfect interobserver agreement. Thus, DTD-TIRADS can be considered to be an effective classification system for diagnosing DTD.

Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study.

BACKGROUND: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated. RESEARCH QUESTION: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis. STUDY DESIGN AND METHODS: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality. RESULTS: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90). INTERPRETATION: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED.

VI-RADS: Multiinstitutional Multireader Diagnostic Accuracy and Interobserver Agreement Study.

BACKGROUND. The Vesical Imaging Reporting and Data System (VI-RADS), based on multiparametric MRI (mpMRI), was developed to provide accurate information for the diagnosis of muscle-invasive bladder cancer (MIBC). OBJECTIVE. The purpose of our study was to evaluate the interobserver agreement and diagnostic performance of VI-RADS among readers with different levels of experience. METHODS. This retrospective study included 91 consecutive patients who underwent mpMRI before transurethral resection of bladder tumor (TURBT) from July 2010 through August 2018. After attending a training session, seven radiologists (five radiologists experienced in bladder MRI and two inexperienced radiologists) reviewed and scored all MRI examinations according to VI-RADS. The interobserver agreement was assessed by kappa statistics. ROC analysis was used to evaluate the diagnostic performance for MIBC. AUCs were estimated. RESULTS. Among 91 patients (72 men and 19 women; mean age ± SD, 73.2 ± 10.2 years), 48 (52.7%) had MIBC and 43 (47.3%) had non-muscle-invasive bladder cancer. Sixty-eight patients were treated with TURBT, and 23 were treated with radical cystectomy. Interobserver agreement was moderate to substantial (κ = 0.60-0.80) among the experienced readers, substantial (κ = 0.67) between the two inexperienced readers, and moderate to substantial (κ = 0.55-0.75) between the experienced and inexperienced readers. The pooled AUC was 0.88 (range, 0.82-0.91) for experienced readers and 0.84 (range, 0.83-0.85) for inexperienced readers, and 0.87 for all readers. Using a VI-RADS score of 4 or greater as the cutoff value for MIBC, the pooled sensitivity and specificity were 74.1% (range, 66.0-80.9%) and 94.1% (range, 88.6-97.7%) for experienced readers and 63.9% (range, 59.6-68.1%) and 86.4% (range, 84.1-88.6%) for inexperienced readers. Using a VI-RADS score of 3 or greater as the cutoff value, the pooled sensitivity and specificity were 83.4% (range, 80.9-85.1%) and 77.3% (range, 61.4-88.6%) for experienced readers and 82.0% (range, 80.9-83.0%) and 73.9% (range, 72.7-75.0%) for inexperienced readers. CONCLUSION. We observed moderate to substantial interobserver agreement and a pooled AUC of 0.87 among radiologists of different levels of expertise using VI-RADS. CLINICAL IMPACT. VI-RADS could help determine the depth and range of excision in TURBT, decreasing the risk of complications and enhancing the accuracy of pathologic diagnosis.

Diagnostic Accuracy and Interobserver Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers.

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.

Common Data Elements in Head and Neck Radiology Reporting.

Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems.

A comparison of manually populated radiology information system digital radiographic data with electronic dose management systems.

OBJECTIVE: To assess the accuracy and agreement of radiology information system (RIS) kerma-area product (KAP) data with respect to automatically populated dose management system (DMS) data for digital radiography (DR). METHODS: All adult radiographic examinations over 12 months were exported from the RIS and DMS at three centres. Examinations were matched by unique identifier fields, and grouped by examination type. Each centre's RIS sample completeness was calculated, as was the percentage of the RIS examination KAP values within 5% of their DMS counterparts (used as an accuracy metric). For each centre, the percentage agreement between the RIS and DMS examination median KAP values was computed using a Bland-Altman analysis. At two centres, up to 42.5% of the RIS KAP units entries were blank or invalid; corrections were attempted to improve data quality in these cases. RESULTS: Statistically significant intersite variation was seen in RIS data accuracy and the agreement between the uncorrected RIS and DMS median KAP data, with a Bland-Altman bias of up to 11.1% (with a -31.7% to 53.9% 95% confidence interval) at one centre. Attempts to correct invalid KAP units increased accuracy but produced worse agreement at one centre, a slight improvement at another and no significant change in the third. CONCLUSION: The RIS data poorly represented the DMS data. ADVANCES IN KNOWLEDGE: RIS KAP data are a poor surrogate for DMS data in DR. RIS data should only be used in patient dose surveys with an understanding of its limitations and potential inaccuracies.

Possible solution for the problem of unread image interpretation reports: the "Gunma University Star Search".

PURPOSE: To propose a new strategy to prevent communication errors caused by unread radiology reports. MATERIALS AND METHODS: Medical emergencies were prefixed with triple stars on radiology reports, and the attending physician was contacted by telephone. Semi-emergencies (medical issues needing addressing within 2 weeks) were prefixed with double stars. Two weeks later, the duty radiologist would search the double-starred reports, and reviewed relevant patient charts to confirm that the information had been appropriately understood and acted upon. If not, the duty radiologist contacted the referral physician by telephone. One year after implementing this strategy, we retrospectively evaluated 1-year worth of data for all the reports of CT, MRI, nuclear medicine and ultrasonography (April 2018 to March 2019). RESULTS: Three hundred and twenty-one reports were double starred (0.52% of 62,143 reports, 1.32 reports/day), and transmission of relevant information was incomplete in 23 cases (7.17%). Causes of incomplete transmission were (1) reports not being opened (n = 17), (2) relevant information on reports being overlooked (n = 5), and (3) the wrong report being opened (n = 1). Sixty-five reports contained triple stars (0.10%, 0.27 reports/day). CONCLUSION: The proposed strategy may be effective in preventing communication errors in radiology reports with important findings requiring semi-emergency clinical action.

Starviewer and its comparison with other open-source DICOM viewers using a novel hierarchical evaluation framework.

METHODS: The aim of the paper is twofold. First, we present Starviewer, a DICOM viewer developed in C++ with a core component built on top of open-source libraries. The viewer supports extensions that implement functionalities and front-ends for specific use cases. Second, we propose an adaptable evaluation framework based on a set of criteria weighted according to user needs. The framework can consider different user profiles and allow criteria to be decomposed in subcriteria and grouped in more general categories making a multi-level hierarchical structure that can be analysed at different levels of detail to make scores interpretation more comprehensible. RESULTS: Different examples to illustrate Starviewer functionalities and its extensions are presented. In addition, the proposed evaluation framework is used to compare Starviewer with four open-source viewers regarding their functionalities for daily clinical practice. In a range from 0 to 10, the final scores are: Horos (7.7), Starviewer (6.2), Weasis (6.0), Ginkgo CADx (4.1), and medInria (3.8). CONCLUSIONS: Starviewer provides basic and advanced features for daily image diagnosis needs as well as a modular design that enables the development of custom extensions. The evaluation framework is useful to understand and prioritize new development goals, and can be easily adapted to express different needs by altering the weights. Moreover, it can be used as a complement to maturity models.

Improving Billing Accuracy Through Enterprise-Wide Standardized Structured Reporting With Cross-Divisional Shared Templates.

OBJECTIVE: We describe our experience in implementing enterprise-wide standardized structured reporting for chest radiographs (CXRs) via change management strategies and assess the economic impact of structured template adoption. METHODS: Enterprise-wide standardized structured CXR reporting was implemented in a large urban health care enterprise in two phases from September 2016 to March 2019: initial implementation of division-specific structured templates followed by introduction of auto launching cross-divisional consensus structured templates. Usage was tracked over time, and potential radiologist time savings were estimated. Correct-to-bill (CTB) rates were collected between January 2018 and May 2019 for radiography. RESULTS: CXR structured template adoption increased from 46% to 92% in phase 1 and to 96.2% in phase 2, resulting in an estimated 8.5 hours per month of radiologist time saved. CTB rates for both radiographs and all radiology reports showed a linearly increasing trend postintervention with radiography CTB rate showing greater absolute values with an average difference of 20% throughout the sampling period. The CTB rate for all modalities increased by 12%, and the rate for radiography increased by 8%. DISCUSSION: Change management strategies prompted adoption of division-specific structured templates, and exposure via auto launching enforced widespread adoption of consensus templates. Standardized structured reporting resulted in both economic gains and projected radiologist time saved.

RadLex Normalization in Radiology Reports.

Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Further, no study to date has attempted to leverage RadLex for standardization. In this paper, we aim to normalize a diverse set of radiological entities to RadLex terms. We manually construct a normalization corpus by annotating entities from three types of reports. This contains 1706 entity mentions. We propose two deep learning-based NLP methods based on a pre-trained language model (BERT) for automatic normalization. First, we employ BM25 to retrieve candidate concepts for the BERT-based models (re-ranker and span detector) to predict the normalized concept. The results are promising, with the best accuracy (78.44%) obtained by the span detector. Additionally, we discuss the challenges involved in corpus construction and propose new RadLex terms.

Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance.

Hospitals often set protocols based on well defined standards to maintain the quality of patient reports. To ensure that the clinicians conform to the protocols, quality assurance of these reports is needed. Patient reports are currently written in free-text format, which complicates the task of quality assurance. In this paper, we present a machine learning based natural language processing system for automatic quality assurance of radiology reports on breast cancer. This is achieved in three steps: we i) identify the top-level structure (headings) of the report, ii) classify the report content into the top-level headings, and iii) convert the free-text detailed findings in the report to a semi-structured format (post-structuring). Top level structure and content of report were predicted with an F1 score of 0.97 and 0.94, respectively, using Support Vector Machine (SVM) classifiers. For automatic structuring, our proposed hierarchical Conditional Random Field (CRF) outperformed the baseline CRF with an F1 score of 0.78 versus 0.71. The determined structure of the report is represented in semi-structured XML format of the free-text report, which helps to easily visualize the conformance of the findings to the protocols. This format also allows easy extraction of specific information for other purposes such as search, evaluation, and research.

Structured Reporting in Ultrasound.

Structured reporting of ultrasound examinations can add value throughout the imaging chain. Reports may be created in a more efficient manner, with increased accuracy and clarity. Communication with referring providers and patients may be improved. Patient care can be enhanced through improved adherence with guidelines and local best practices. Radiology departments may benefit from improved billing and quality reporting. Consistent discrete data can enable research and collaborations between institutions. This article will review the multifaceted impact of structuring ultrasound reports.

Standardized reporting systems for computed tomography coronary angiography and calcium scoring: A real-world validation of CAD-RADS and CAC-DRS in patients with stable chest pain.

OBJECTIVES: To assess the prognostic implications of standardized reporting systems for coronary computed tomography angiography (CCTA) and coronary artery calcium scores (CACS) in patients with stable chest pain. BACKGROUND: The Coronary Artery Disease Reporting And Data System (CAD-RADS) and Coronary Artery Calcium - Data and Reporting System (CAC-DRS) aim to improve communication of CACS and CCTA results, but its influence on prognostication is unknown. METHODS: Images from 1769 patients who underwent CCTA as part of the Scottish Computed Tomography of the HEART (SCOT-HEART) multi-center randomized controlled trial were assessed. CACS were classified as CAC-DRS 0 to 3 based on Agatston scores. CCTA were classified as CAD-RADS 0 to 5 based on the most clinically relevant finding per patient. The primary outcome was the five-year events of fatal and non-fatal myocardial infarction. RESULTS: Patients had a mean age of 58 ±â€¯10 years and 56% were male. CAC-DRS 0, 1, 2 and 3 occurred in 642 (36%), 510 (29%), 239 (14%) and 379 (21%) patients respectively. CAD-RADS 0, 1, 2, 3, 4A, 4B and 5 occurred in 622 (35%), 327 (18%), 211 (12%), 165 (9%), 221 (12%), 42 (2%) and 181 (10%) patients respectively. Patients classified as CAC-DRS 3 were at an increased risk of fatal or non-fatal myocardial infarction compared to CAC-DRS 0 patients (hazard ratio (HR) 9.41; 95% confidence interval (CI) 3.24, 27.31; p < 0.001). Patients with higher CAD-RADS categories were at an increased risk of fatal or non-fatal myocardial infarction, with patients classified as CAD-RADS 4B at the highest risk compared to CAD-RADS 0 patients (HR 19.14; 95% CI 4.28, 85.53; p < 0.001). CONCLUSION: Patients with higher CAC-DRS and CAD-RADS scores were at increased risk of subsequent fatal and non-fatal myocardial infarction. This confirms that the classification provides additional prognostic discrimination for future coronary heart disease events.

The use of structured reporting of head and neck ultrasound ensures time-efficiency and report quality during residency.

PURPOSE: Free text reports (FTR) of head and neck ultrasound studies are currently deployed in most departments. Because of a lack of composition and language, these reports vary greatly in terms of quality and reliability. This may impair the learning process during residency. The purpose of the study was to analyze the longitudinal effects of using structured reports (SR) of head and neck ultrasound studies during residency. METHODS: Attending residents (n = 24) of a tripartite course on head and neck ultrasound, accredited by the German Society for Ultrasound in Medicine (DEGUM), were randomly allocated to pictures of common diseases. Both SRs and FTRs were compiled. All reports were analyzed concerning completeness, acquired time and legibility. Overall user contentment was evaluated by a questionnaire. RESULTS: SRs achieved significantly higher ratings regarding completeness (95.6% vs. 26.4%, p < 0.001), description of pathologies (72.2% vs. 58.9%, p < 0.001) and legibility (100% vs. 52.4%, p < 0.001) with a very high inter-rater reliability (Fleiss' kappa 0.9). Reports were finalized significantly faster (99.1 s vs. 115.0 s, p < 0.001) and user contentment was significantly better when using SRs (8.3 vs. 6.3, p < 0.001). In particular, only SRs showed a longitudinally increasing time efficiency (- 20.1 s, p = 0.036) while maintaining consistent completeness ratings. CONCLUSIONS: The use of SRs of head and neck ultrasound studies results in an increased longitudinal time-efficiency while upholding the report quality at the same time. This may indicate an additive learning effect of structured reporting. Superior outcomes in terms of comprehensiveness, legibility and time-efficiency can be observed immediately after implementation.

Performance of primary diagnostic monitors (PDMs) over time.

In this work, we evaluated the change of primary monitor characteristics in two consecutive years. Sixty-six primary monitors were included in the analysis. The monitors were located at radiology physicians' offices and radiology reading rooms. All primary monitors were equipped with the manufacturer's built-in photometers and connected to the BarcoMediCalQA web service for manual and automatic quality control measurements. External photometer/illuminance meter (RaySafe Solo Light) was used to measure the luminance values. Measured luminance values of the TG18LN1-18 and TG18UNL80 test patterns were used to evaluate the primary monitors performance. In a comparison of the quality assurance (QA) measurement results for the same monitors that were performed within 2 years, the luminance of 25 displays remained statistically the same (P > 0.01). The luminance of 17 displays decreased (P < 0.01) in 2017 when compared with 2016, the luminance of 24 displays increased (P < 0.01) in 2017 when compared with 2016. For the annual measurements of the MLD in 2016 and 2017, 25 out of 66 displays showed a decrease of MLD values in 2017 compared with the same measurements in 2016 and 41 displays showed an increase of MLD in 2017. All tested primary displays had the MLD value less than 17.2%. The mean value of illuminance measured in 2016 was 5.8 lux ± 3.1 lux. In 2017, the mean value of illuminance measured was 8.7 lux ± 5.3 lux. Although it is expected that monitors luminance values will decrease over time, we found displays with increased luminance. This is possibly due to the multiple monitor calibrations that were performed between two annual monitor QA tests. Based on the findings of this work, more efficient display QA programs with a shorter time interval than 1 year are needed.