Corrigendum to "Exploring the uncommon: Unusual instance of retained fractured needles in a patient of intravenous drugs abuse" [Radiology Case Reports 19 (2024) 1619-1623]

[This corrects the article DOI: 10.1016/j.radcr.2024.01.016.].

Utility of Dual-Energy Computed Tomography in Clinical Conundra

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Exploring the uncommon: Unusual instance of retained fractured needles in a patient of intravenous drugs abuse

Retained needle fragments commonly serve as sources of recurrent infections with a potential to embolize to the heart and lungs and can lead to life-threatening consequences. Here, we report a case of a 46-year-old male with a history of intravenous drug user and chronic forearm wounds, presenting with sepsis. Several retained needles are identified on CT scan, several days postadmission. This case highlights the importance of timely assessment of infectious sources in patients with history of intravenous drug abuse.

Frequently Encountered Artifacts in the Application of Dual-Energy Computed Tomography to Cardiovascular Imaging for Urate Crystals in Gout: A Matched-Control Study

OBJECTIVE

There is surging interest in using dual-energy computed tomography (DECT) to identify cardiovascular monosodium urate (MSU) deposits in patients with gout. We sought to examine the prevalence and characterization of cardiovascular DECT artifacts using non-electrocardiogram (EKG)-gated DECT pulmonary angiograms.

METHODS

We retrospectively reviewed non-EKG-gated DECT pulmonary angiograms performed on patients with and without gout at a single academic center. We noted the presence and locations of vascular green colorization using the default postprocessing two-material decomposition algorithm for MSU. The high- and low-energy grayscale images and advanced DECT measurements were used to determine whether they were true findings or artifacts. We classified artifacts into five categories: streak, contrast medium mixing, misregistration due to motion, foreign body, and noise.

RESULTS

Our study included CT scans from 48 patients with gout and 48 age- and sex-matched controls. The majority of patients were male with a mean age of 67 years. Two independent observers attributed all areas of vascular green colorization to artifacts. The most common types of artifacts were streak (56% vs 57% between patients and controls, respectively) and contrast medium mixing (51% vs 65%, respectively). Whereas some of the default DECT measurements of cardiovascular green colorization were consistent with values reported for subcutaneous tophi, advanced DECT measurements were not consistent with that of tophi.

CONCLUSION

Artifacts that could be misconstrued as cardiovascular MSU deposits were commonly identified in patients with and without gout on non-EKG-gated DECT pulmonary angiograms. These artifacts can inform future vascular DECT studies on patients with gout to minimize false-positive findings.

Establishing and Leading a 3D Postprocessing Radiology Lab: A Managerial and Leadership Perspective

The rapid acquisition of larg volumes of thin-section CT images has created a considerable need and interest for 3D postprocessing during the interpretation of medical imaging. As a result of the increasing number of postprocessing applications, requiring diagnostic radiologists to perform postprocessing is no longer realistic. This article is a comprehensive review of medical resources regarding establishing a postprocessing radiology laboratory. Besides, leadership and managerial aspects have been covered through a professional business lens. In large-volume settings, a dedicated 3D postprocessing lab ensures the quality, reproducibility, and efficiency of images. Adequate staffing is necessary to fulfill the postprocessing requirements. Educational and experience requirements for 3D technologists may vary among different running laboratories. To evaluate the establishment and running of a 3D lab, it is beneficial to implement diagnostic radiology cost-effectiveness tools. Although establishing a 3D lab has many benefits, certain challenges should be considered. Outsourcing or offshoring may serve as alternatives for establishing a postprocessing laboratory. Building and operating a 3D lab is a significant change in healthcare facilities, and it is crucial for organizations to be aware of the strong resistance toward alternatives the status quo, known as the status quo trap. The change process has essential steps, and skipping the steps creates an illusion of speed but never produces satisfactory results. The organization should ensure the engagement of all interested parties in the whole process. Moreover, a clear vision and proper communication of the vision are vital, and it is crucial to value small wins and ensure expectation clarity in leading the lab during the process.

The Role of Dual-Energy CT in Solid Organ Injury

The liver, spleen, and kidneys are the commonest injured solid organs in blunt and penetrating trauma. The American Association for the Surgery of Trauma (AAST) Organ Injury Scale (OIS) is the most widely accepted system for categorizing traumatic injuries. Grading systems allow clear communication of findings between clinical teams and assign a measurable severity of injury, which directly correlates with morbidity and mortality. The 2018 revised AAST OIS emphasizes reliance on CT for accurate grading; in particular regarding vascular injuries. Dual-Energy CT (DECT) has emerged as a promising tool with multiple clinical applications already demonstrated. In this review article, we summarize the basic principles of CT attenuation to refresh the minds of our readers and we scrutinize DECT's technology as opposed to conventional Single-Energy CT (SECT). This is followed by outlining the benefits of various DECT postprocessing techniques, which authors of this article refer to as the 3Ms (Mapping of Iodine, Material decomposition, and Monoenergetic virtual imaging), in aiding radiologists to confidently assign an OIS as well as problem solve complex injury patterns. In addition, a thorough discussion of changes to the revised AAST OIS focusing on definitions of key terms used in reporting injuries is described.

Underreported and underrecognized: a comprehensive imaging review of breast injury

Breast injury is commonly encountered yet it remains significantly underreported. Injury to the breast may arise from either primary mechanisms or secondary or iatrogenic mechanisms. Primary mechanisms of breast injury include blunt force, seat-belt, penetrating, and thermal injury. Secondary or iatrogenic mechanisms of breast injury include breast biopsy or intervention as well as operative intervention and cardiopulmonary resuscitation. The severity of breast injury arising from these mechanisms is broad, ranging from breast contusion to avulsion. Sequelae of breast injury include fat necrosis and Mondor's disease. Radiologists play an integral role in the evaluation and management of breast injury both in the acute and non-acute settings. In the acute setting, radiologists must be able to recognize breast injury arising from primary mechanisms or iatrogenic or secondary mechanisms and to identify rare but potentially life-threatening complications promptly to ensure timely, appropriate management. In the non-acute setting, radiologists must be able to discern the sequalae of breast injury from other processes to prevent potentially unnecessary further evaluation and intervention. Nonetheless, though breast injury is commonly encountered there remain few guidelines and a lack of established recommendations for the evaluation and management of breast injury. We provide a comprehensive multi-modality imaging review of breast injury arising in the acute setting as well as the sequela of breast injury arising in the non-acute setting. Moreover, we provide an overview of the management of breast injury.

Dual-Energy Computed Tomography and Beyond: Musculoskeletal System

Traditional monoenergetic computed tomography (CT) scans in musculoskeletal imaging provide excellent detail of bones but are limited in the evaluation of soft tissues. Dual-energy CT (DECT) overcomes many of the traditional limitations of CT and offers anatomical details previously seen only on MR imaging. In addition, DECT has benefits in the evaluation and characterization of arthropathies, bone marrow edema, and collagen applications in the evaluation of tendons, ligaments, and vertebral discs. There is current ongoing research in the application of DECT in arthrography and bone mineral density calculation.

Phase 2 Dose-Finding Study in Patients with Gout Using SEL-212, a Novel PEGylated Uricase (SEL-037) Combined with Tolerogenic Nanoparticles (SEL-110)

INTRODUCTION

SEL-212 is a developmental treatment for uncontrolled gout characterized by serum uric acid (sUA) levels ≥ 6 mg/dl despite treatment. It comprises a novel PEGylated uricase (SEL-037; also called pegadricase) co-administered with tolerogenic nanoparticles containing sirolimus (rapamycin) (SEL-110; also called ImmTOR®), which mitigates the formation of anti-drug antibodies (ADAs) against uricase and SEL-037 (PEGylated uricase), thereby enabling sustained sUA control (sUA < 6 mg/dl). The aim of this study was to identify appropriate dosing for SEL-037 and SEL-110 for use in phase 3 clinical trials.

METHODS

This open-label phase 2 study was conducted in adults with symptomatic gout and sUA ≥ 6 mg/dl. Participants received five monthly infusions of SEL-037 (0.2 or 0.4 mg/kg) alone or in combination with three or five monthly infusions of SEL-110 (0.05-0.15 mg/kg). Safety, tolerability, sUA, ADAs, and tophi were monitored for 6 months.

RESULTS

A total of 152 adults completed the study. SEL-037 alone resulted in rapid sUA reductions that were not sustained beyond 30 days in most participants due to ADA formation and loss of uricase activity. Levels of ADAs decreased with increasing doses of SEL-110 up to 0.1 mg/kg, with anti-uricase titers < 1080 correlating with sustained sUA control and reductions in tophi. Overall, 66% of evaluable participants achieved sUA control at week 20 following five monthly doses of SEL-037 0.2 mg/kg + SEL-110 0.1-0.15 mg/kg, whereas only 26% achieved sUA control at week 20 when SEL-110 was withdrawn after week 12. Compared to other dose combinations, SEL-037 0.2 mg/kg + SEL-110 0.15 mg/kg achieved the greatest sUA control at week 12 and was well-tolerated with no safety concerns.

CONCLUSION

Results provide continued support for the use of multiple monthly administrations of SEL-037 0.2 mg/kg + SEL-110 0.1-0.15 mg/kg in clinical trials for SEL-212.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT02959918.

Gastropulmonary fistula following sleeve gastrectomy: use of dual-energy CT following oral contrast administration to confirm diagnosis

Gastropulmonary fistula represents a late complication of sleeve gastrectomy and, if untreated, has high morbidity and mortality. We present a case report of a 29-year-old female who developed a gastropulmonary fistula 3 years after a sleeve gastrectomy. Dual energy CT of the chest and upper abdomen demonstrated a cavitary left lower lobe lesion associated with a focal complex pleural effusion; iodinated oral contrast confirmed the presence of a fistulous connection through the left hemidiaphragm. The patient underwent a thoracotomy, left lower lobectomy, resection of the infected segment of the left hemidiaphragm with primary repair, drainage of a subphrenic abscess and a gastric repair; the patient was discharged 2-weeks postprocedure.

Effect of COVID-19 pandemic on utilisation of community-based mental health care in North-East of Italy: A psychiatric case register study

AIMS

WHO declared that mental health care should be considered one essential health service to be maintained during the coronavirus disease 2019 (COVID-19) pandemic. This study aims to describe the effect of lockdown and restrictions due to the COVID-19 pandemic in Italy on mental health services' utilisation, by considering psychiatric diagnoses and type of mental health contacts.

METHODS

The study was conducted in the Verona catchment area, located in the Veneto region (northeastern Italy). For each patient, mental health contacts were grouped into: (1) outpatient care, (2) social and supportive interventions, (3) rehabilitation interventions, (4) multi-professional assessments, (5) day care. A 'difference in differences' approach was used: difference in the number of contacts between 2019 and 2020 on the weeks of lockdown and intermediate restrictions was compared with the same difference in weeks of no or reduced restrictions, and such difference was interpreted as the effect of restrictions. Both a global regression on all contacts and separate regressions for each type of service were performed and Incidence Rate Ratios (IRRs) were calculated.

RESULTS

In 2020, a significant reduction in the number of patients who had mental health contacts was found, both overall and for most of the patients' characteristics considered (except for people aged 18-24 years for foreign-born population and for those with a diagnosis of schizophrenia. Moreover, in 2020 mental health contacts had a reduction of 57 096 (-33.9%) with respect to 2019; such difference remained significant across the various type of contacts considered, with rehabilitation interventions and day care showing the greatest reduction. Negative Binomial regressions displayed a statistically significant effect of lockdown, but not of intermediate restrictions, in terms of reduction in the number of contacts. The lockdown period was responsible of a 32.7% reduction (IRR 0.673; p-value <0.001) in the overall number of contacts. All type of mental health contacts showed a reduction ascribable to the lockdown, except social and supportive interventions.

CONCLUSIONS

Despite the access to community mental health care during the pandemic was overall reduced, the mental health system in the Verona catchment area was able to maintain support for more vulnerable and severely ill patients, by providing continuity of care and day-by-day support through social and supportive interventions.

Dual Energy Computed Tomography Collagen Material Decomposition for Detection of Lumbar Spine Disc Extrusion and Sequestration: A Comparative Study With Greyscale Computed Tomography

Purpose: To assess value of dual energy computed tomography (DECT) collagen material decomposition algorithm when combined with standard computed tomography (CT) in detection of lumbar disc extrusion and sequestration. Materials and Methods: Retrospective analysis of all patients with acute low back pain who had a diagnosis of lumbar spine disc extrusion and/or sequestration on Magnetic Resonance Imaging (MRI) (reference standard), and had undergone non-contrast DECT of the lumbar spine within 60 days of the MRI. Age and sex-matched control patients (n = 42) were included. Patients were grouped into standard, grey-scale CT only group and standard CT + DECT tendon images group. Two double-blinded radiologists reviewed both groups for presence of extrusion or sequestration. They also rated their diagnostic confidence on Likert 5-point scale. McNemar Chi-square test was used to compare diagnostic accuracy, unpaired t-test to compare reviewers diagnostic confidence, and Cohen's k (kappa) test for interobserver agreement. Results: The combined group showed higher overall sensitivity (96.6% vs 87.2%), specificity (99% vs 95.4%), and diagnostic accuracy (98.7% vs 94.5%) with a lower false positive rate (1.1% vs 4.6%). McNemar Chi-square test confirmed statistical significance (P = .03 and P = .02 for Reviewers R1 and R2, respectively). The mean diagnostic confidence was also significantly higher on combined group (R1: 3.74 ± 1.1 vs 3.47 ± 1.15 (P < .01) and R2: 3.91 ± 1.15 vs 3.72 ± 1.16 [mean ± SD] (P = .02)). Conclusion: Utilizing MRI as a reference standard, DECT tendon application combined with standard CT increases the sensitivity, specificity, and accuracy of detection of lumbar spine disc extrusion and sequestration, when compared to standard CT alone.

Starting an Emergency Radiology Division: Scheduling and Staffing, Compensation, and Equity and Parity

Establishing an emergency radiology division in a practice that has long-standing patterns of operational routines comes with both challenges and opportunities. In this article, considerations around scheduling and staffing, compensation, and equity and parity are provided with supporting literature references. Furthermore, a panel of experts having established, grown and managed emergency radiology divisions in North America and Europe share their experiences through a question and answer format.

Machine Learning Model for Chest Radiographs: Using Local Data to Enhance Performance

PURPOSE

To develop and assess the performance of a machine learning model which screens chest radiographs for 14 labels, and to determine whether fine-tuning the model on local data improves its performance. Generalizability at different institutions has been an obstacle to machine learning model implementation. We hypothesized that the performance of a model trained on an open-source dataset will improve at our local institution after being fine-tuned on local data.

METHODS

In this retrospective, institutional review board approved study, an ensemble of neural networks was trained on open-source datasets of chest radiographs for the detection of 14 labels. This model was then fine-tuned using 4510 local radiograph studies, using radiologists' reports as the gold standard to evaluate model performance. Both the open-source and fine-tuned models' accuracy were tested on 802 local radiographs. Receiver-operator characteristic curves were calculated, and statistical analysis was completed using DeLong's method and Wilcoxon signed-rank test.

RESULTS

The fine-tuned model identified 12 of 14 pathology labels with area under the curves greater than .75. After fine-tuning with local data, the model performed statistically significantly better overall, and specifically in detecting six pathology labels (P < .01).

CONCLUSIONS

A machine learning model able to accurately detect 14 labels simultaneously on chest radiographs was developed using open-source data, and its performance was improved after fine-tuning on local site data. This simple method of fine-tuning existing models on local data could improve the generalizability of existing models across different institutions to further improve their local performance.

Magnetic resonance imaging predictors (cartilage, osteophytes and meniscus) of prevalent and 3-year incident medial and lateral tibiofemoral knee joint tenderness and patellofemoral grind

Objective

To identify magnetic resonance imaging (MRI) predictors (cartilage [C], osteophytes [O] and meniscus [M] scores) of prevalent and 3-year incident medial tibiofemoral (MTF) and lateral tibiofemoral (LTF) knee joint tenderness and patellofemoral (PF) grind. 

Methods

Population-based knee pain cohort aged 40–79 was assessed at baseline (N = 255), 3- and 7-year follow-up (N = 108 × 2 = 216). COM scores were measured at 6/8/6 subregions respectively. Age-sex-BMI adjusted logistic models predicted prevalence versus relevant COM predictors (medial, lateral or patellar / trochlear groove scores). Fully adjusted models also included all relevant COM predictors. Binary generalized estimating equations models predicting 3-year incidence were also adjusted for individual follow-up time between cycles.

Results

Significant predictors of prevalent MTF tenderness: medial femoral cartilage (fully adjusted odds ratio [aOR] 1.84; 95% confidence interval [CI] 1.11, 3.05), female (aOR = 3.05; 1.67, 5.58), BMI (aOR = 1.53 per 5 units BMI; 1.10, 2.11). Predictors of prevalent LTF tenderness: female (aOR = 2.18; 1.22, 3.90). There were no predictors of prevalent PF grind in the fully adjusted model. However, medial patellar osteophytes was predictive in the age-sex-BMI adjusted model. There were no predictors of 3-year incident MTF tenderness. Predictors of 3-year incident LTF tenderness: female (aOR = 3.83; 1.25, 11.77). Predictors of 3-year incident PF grind: lateral patellar osteophytes (aOR = 4.82; 1.69, 13.77). In the age-sex-BMI adjusted model, patellar cartilage was also a predictor.

Conclusion

We explored potential MRI predictors of prevalent and 3-year incident MTF/LTF knee joint tenderness and PF grind. These findings could guide preemptive strategies aimed at reducing these symptoms in the present and future (3-year incidence).

Association between statins and progression of osteoarthritis features on magnetic resonance imaging in a predominantly pre-radiographic cohort: the Vancouver Longitudinal Study of Early Knee Osteoarthritis (VALSEKO): a cohort study

Background

To evaluate the effect of statin use on osteoarthritis (OA) incidence/progression using magnetic resonance imaging (MRI) in a population-based cohort with predominantly pre-radiographic knee OA.

Methods

A cohort aged 40–79 years with knee pain was recruited using random population sampling and followed for 7 years. Baseline exclusions were inflammatory arthritis, recent knee surgery/injury, and inability to undergo MRI. At baseline, current statin use was ascertained. Baseline and follow-up MRIs were read semi-quantitatively for cartilage damage (grade 0–4, 0/1 collapsed, 6 regions), osteophytes (grade 0–3, 8 regions), bone marrow lesions (BML) (grade 0–3, 6 regions) and effusion (grade 0–3). The primary outcome was cartilage damage incidence/progression, while secondary outcomes were incidence/progression of osteophytes, BML, and effusion, each defined as an increase by ≥1 grade at any region. To ensure population representative samples, sample weights were used. Logistic regression was used to assess the association of statin use at baseline with incidence/progression of MRI outcomes. Analyses were adjusted for sex, age, BMI, and multiple comorbidities requiring statin therapy.

Results

Of 255 participants evaluated at baseline, 122 completed the 7-year follow-up. Statin use was not significantly associated with progression of cartilage damage (OR 0.82; 95% CI 0.17, 4.06), osteophytes (OR 3.48; 95% CI 0.40, 30.31), BML (OR 0.61; 95% CI 0.12, 3.02), or effusion (OR 2.38; 95% CI 0.42, 13.63), after adjusting for confounders.

Conclusion

In this population-based cohort of predominantly pre-radiographic knee OA, statins did not affect MRI incidence/progression of cartilage damage, BML, osteophytes or effusion. Therefore, statin use does not appear to affect people with pre-radiographic stages of knee OA.

Bowel obstruction and perforation secondary to progressive heterotopic mesenteric ossificans

Heterotopic mesenteric ossification (HMO) is a rare condition which usually affects male patients. Its defining feature is hyperdense ossification in the mesentery, usually following surgery or trauma. Due to potentially serious complications that can arise from HMO, it is essential to recognize it in its nascent stages. In this case study, a 65-year-old male was imaged by CT scan serially over several years for recurrent bowel obstruction as a result of worsening HMO, providing new insight into the natural progression of this condition. Mechanical injury of the bowel eventually caused perforation and abscess formation.

A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia

RATIONALE AND OBJECTIVES

Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden.

MATERIALS AND METHODS

We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL.

RESULTS

Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349).

CONCLUSION

Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans

Purpose

To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools.

Materials and Methods

This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; n = 25 946) and evaluation (n = 2947) and three "external" centers for calibration (n = 400) and evaluation (n = 16764). Internal centers contributed developmental data, whereas external centers did not. Deep neural networks predicted the presence of ICH and subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and/or epidural hemorrhage) and segmentations per case. Two ICH confidence scores are discussed: a calibrated classifier entropy score and a Dempster-Shafer score. Evaluation was completed by using receiver operating characteristic curve analysis and report turnaround time (RTAT) modeling on the evaluation set and on confidence score-defined subsets using bootstrapping.

Results

The areas under the receiver operating characteristic curve for ICH were 0.97 (0.97, 0.98) and 0.95 (0.94, 0.95) on internal and external center data, respectively. On 80% of the data stratified by calibrated classifier and Dempster-Shafer scores, the system improved the Youden indexes, increasing them from 0.84 to 0.93 (calibrated classifier) and from 0.84 to 0.92 (Dempster-Shafer) for internal centers and increasing them from 0.78 to 0.88 (calibrated classifier) and from 0.78 to 0.89 (Dempster-Shafer) for external centers (P < .001). Models estimated shorter RTAT for AI-prioritized worklists with confidence measures than for AI-prioritized worklists without confidence measures, shortening RTAT by 27% (calibrated classifier) and 27% (Dempster-Shafer) for internal centers and shortening RTAT by 25% (calibrated classifier) and 27% (Dempster-Shafer) for external centers (P < .001).

Conclusion

AI that provided statistical confidence measures for ICH detection on NCCT scans reliably detected and subtyped hemorrhages, identified high-confidence predictions, and improved worklist prioritization in simulation.Keywords: CT, Head/Neck, Hemorrhage, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Emergency Radiology: Evolution, Current Status, and Future Directions

Emergency Radiology is a clinical practice and an academic discipline that has rapidly gained increasing global recognition among radiology and emergency/critical care departments and trauma services around the world. As with other subspecialties, Emergency Radiology practice has a unique scope and purpose and presents with its own unique challenges. There are several advantages of having a dedicated Emergency Radiology section, perhaps most important of which is the broad clinical skillset that Emergency Radiologists are known for. This multi-society paper, representing the views of Emergency Radiology societies in Canada and Europe, outlines several value-oriented contributions of Emergency Radiologists and briefly discusses the current state of Emergency Radiology as a subspecialty.

Dual-Energy CT in the Acute Setting: Bowel Trauma

Traumatic bowel and mesenteric injuries (TBMI) have significant morbidity and mortality. The physical examination is often limited and sometimes not feasible in the trauma patient. Multidetector CT (MDCT) detection of TBMI is challenging and can be life-saving. Dual-energy CT (DECT) utilizes iodine overlay, monoenergetic imaging, and metal artifact reduction to enhance the conspicuity of TBMI. DECT may improve the conspicuity of TBMI leading to increased diagnostic accuracy and confidence. The aim of the article is to review the state of the art and applications of DECT in bowel trauma.

Cardiac CT angiography after percutaneous left atrial appendage closure: early versus delayed scanning after contrast administration

PURPOSE

Cardiac computed tomography angiography (CCTA) is increasingly used for device surveillance after left atrial appendage closure (LAAC). While CT protocols with delayed scans are useful to diagnose thrombus in the LAA, an optimal protocol for post-procedural CCTA has not been established. Therefore, we assessed the role of delayed versus early scans for device surveillance.

METHODS

We retrospectively reviewed patients who underwent LAAC at Vancouver General Hospital who had follow-up CCTAs using standard (early) and delayed scans. Scans were performed on Toshiba 320-detector (Aquilion ONE). Image quality was interpreted by 2 independent observers for anatomy, LAA contrast patency, and device-related thrombus (DRT) using VitreaWorkstationTM. A Likert scale of 1-5 was used (1= poor quality, 5= excellent) for assessment.

RESULTS

We included 27 consecutive LAAC patients (9 Amplatzer, 18 WATCHMAN) with mean age 76.0±7.7 years, mean CHADS2 score 2.8±1.3, CHA2DS2-VASc score 4.4±1.6 and HAS-BLED score 3.4±1.0. Subjective quality assessments by both reviewers favored early scans for assessment of anatomy (reviewer 1: 4.63±0.63 [early] vs. 1.74±0.71 [delayed]; reviewer 2: 4.63±0.63 [early] vs. 1.89±0.64 [delayed]) and DRT (reviewer 1: 4.78±0.42 [early] vs. 3.11±1.16 [delayed]; reviewer 2: 4.70±0.47 [early] vs. 3.04±1.29 [delayed]). Inter-rater variability showed good correlation between reviewers (intraclass correlation 0.61-0.95). Mean LAA/LA attenuation ratios were significantly different between scans, with larger mean percent reduction of contrast opacification from LA to LAA in the early scans (57.0±36.6% reduction for early vs. 29.1±30.8% for delayed; p < 0.001) CONCLUSION: For CT device surveillance post-LAAC early phase imaging provides superior image quality objectively and subjectively compared with delayed scanning.

Preventing and Mitigating Radiology System Failures: A Guide to Disaster Planning

Disaster planning is a core facet of modern health care practice. Owing to complex infrastructure requirements, radiology departments are vulnerable to system failures that may occur in isolation or during a disaster event when the urgency for and volume of imaging examinations increases. Planning for systems failures helps ensure continuity of service provision and patient care during an adverse event. Hazards to which a radiology department is vulnerable can be identified by applying a systematic approach with recognized tools such as the Hazard, Risk, and Vulnerability Analysis. Potential critical weaknesses within the department are highlighted by the Failure Mode and Effects Analysis tool. Recognizing the potential latent conditions and active failures that may impact systems allows implementation of strategies to prevent failure or to build resilience and mitigate the effects if they happen. Inherent system resilience to an adverse event can be estimated, and the ability of a department to operate during a disaster and the subsequent recovery can be predicted. The main systems at risk in a radiology department are staff, structure, stuff (supplies and/or equipment), and software, although individual issues and solutions within these are department specific. When medical imaging or examination interpretation needs cannot be met in the radiology department, the use of portable imaging modalities and teleradiology can augment the disaster response. All phases of disaster response planning should consider both sustaining operations and the transition back to normal function. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.

A whole-joint, unidimensional, irreversible, and fine-grained MRI knee osteoarthritis severity score, based on cartilage, osteophytes and meniscus (OA-COM)

Objective

To develop a whole-joint, unidimensional, irreversible, and fine-grained MRI knee osteoarthritis (OA) severity score, based on cartilage, osteophytes and meniscus (OA-COM), and to predict progression across different severity states using OA-COM as outcome and clinical variables as predictors.

Methods

Population-based knee pain cohort aged 40–79 was assessed at baseline and 7-year follow-up. OA-COM score was defined as the sum of MRI scores for cartilage, osteophytes and menisci, measured at 6, 8 and 6 sites, total score 0–54. To anchor severity levels, we fit cross-sectional logistic models using OA-COM to predict Kellgren-Lawrence (KL) grades in subsets at or one point below each grade. OA-COM threshold scores were selected on sensitivity, specificity, positive and negative predictive value. We developed longitudinal logistic models for OA-COM progression over each threshold over 7 years. Potential predictors included age, sex, BMI, malalignment, physical exam effusion, quadriceps weakness, and crepitus, selected on area under the receiver operating characteristic curve (AUC) and Akaike’s Information Criterion (AIC).

Results

Optimal OA-COM thresholds were 12, 18, 24 and 30, for KL grades 1 to 4. Significant predictors of progression (depending on threshold) included physical exam effusion, malalignment and female sex, with other selected predictors age, BMI and crepitus.

Conclusion

OA-COM (0–54 range) is a whole-joint, unidimensional, irreversible, and fine-grained MRI OA severity score reflecting cartilage, osteophytes and menisci. OA-COM scores 12, 18, 24 and 30 are equivalent to KL grades 1 to 4, while offering fine-grained differentiation of states between KL grades, and within pre-radiographic disease (KL = 0) or late-stage disease (KL = 4). In modeling, several clinical variables predicted progression across different states over 7 years.

The Effect of Mask Wearing on the Accuracy of Radiology Reports in an Academic Hospital Setting

BACKGROUND

PURPOSE

In response to the pandemic, some public health agencies recommend the wearing of surgical masks in indoor spaces including radiology common reporting rooms. We aim to demonstrate whether mask wearing may lead to increased errors incidence in radiology reports.

MATERIALS AND METHODS

Our prospective studywas conveyed in 2 parts. Firstly, the participants were surveyed if they believed that mask affected dictation. Then participants performed a dictation: they read artificial radiology reports using a commercial voice recognition (VR) system. They performed this task 5 times, each time donning a different mask in random order: a surgical mask, surgical visor, N-95, combination of 2 surgical masks and no mask. Error rates were compared with the Friedman test followed by pairwise Wilcoxon with bootstrapping. Multivariate Poisson regression was performed to test for interaction effects between potential predictors.

RESULTS

52 members of an academic radiology department participatedin the study (January - March 2021) . 65.4% of survey participants did not think or were not sure whether mask wearing could affect dictation process. Treating the no-mask condition as baseline, our study found that mean error rates significantly increased up to 2 times the baseline rate when a surgical mask, surgical visor, N-95 or a combination of 2 masks was donned (p < 0.0001). No significant differences in error rates were found between the different mask types (p > 0.05). Error rates were higher for participants with shorter VR training time (p < 0.0001) or who were non-native English speakers (p < 0.0001). There were no interaction effects between mask type, VR training time or English nativity, suggesting these variables to be independent predictors for error rate. Academic rank did not significantly affect the error rate.

CONCLUSION

radiologists underestimate the influence of masks on dictation accuracy. mask wearing may lead to significant increase in dictational errors.

Impact of 24/7 Onsite Emergency Radiology Staff Coverage on Emergency Department Workflow

PURPOSE

Assess the impact of 24/7/365 emergency trauma radiology (ETR) coverage on Emergency Department (ED) patient flow in an urban, quaternary-care teaching hospital.

METHODS

Patient ED visit and imaging information were extracted from the hospital patient care information system for 2008 to 2018. An interrupted time-series approach with a comparison group was used to study the impact of 24/7/365 ETR on average monthly ED length of stay (ED-LOS) and Emergency Physician to disposition time (EP-DISP). Linear regression models were fit with abrupt and permanent interrupts for 24/7/365 ETR, a coefficient for comparison series and a SARIMA error term; subgroup analyses were performed by patient arrival time, imaging type and chief complaint.

RESULTS

During the study period, there were 949,029 ED visits and 739,796 diagnostic tests. Following implementation of 24/7/365 coverage, we found a significant decrease in EP-DISP time for patients requiring only radiographs (-29 min;95%CI:-52,-6) and a significant increase in EP-DISP time for major trauma patients (46 min;95%CI:13,79). No significant change in patient throughput was observed during evening hours for any patient subgroup. For overnight patients, there was a reduction in EP-DISP for patients with symptoms consistent with stroke (-78 min;95%CI:-131,-24) and for high acuity patients who required imaging (-33 min;95%CI:-57,-10). Changes in ED-LOS followed a similar pattern.

CONCLUSIONS

At our institution, 24/7/365 in-house ETR staff radiology coverage was associated with improved ED flow for patients requiring only radiographs and for overnight stroke and high acuity patients. Major trauma patients spent more time in the ED, perhaps reflecting the required multidisciplinary management.

Unravelling the mysteries of gout by multimodality imaging

Gout is a common inflammatory arthritis that manifests as an aggregate of variably symptomatic monosodium urate crystals (MSU) in the joints and surrounding tissues in addition to multisystem involvement such as genitourinary and cardiovascular systems. In recent decades, there has been a documented increase in the prevalence and incidence of gout. Risk factors for gout include obesity, dietary influences, hypertension, renal impairment, and diuretic use. A prompt diagnosis followed by uric acid lowering treatment prior to the onset of bone destruction is the goal in any suspected case of gout. Advanced imaging modalities, such as dual energy computed tomography (DECT) and ultrasonography (US), employed for the diagnosis of gout are each accompanied by advantages and disadvantages. Conventional radiography (CR), although useful in visualizing joint erosions and mineralization, is limited in its ability to diagnose gout flare. Although synovial fluid aspiration remains the gold standard for MSU crystal visualization, less-invasive imaging modalities are preferred to avoid potential complications. DECT and US in particular are useful in the diagnosis of gout. In this review, we will discuss the current state and role of imaging in the detection of gout.

Urate Crystals; Beyond Joints

Gout is the most common inflammatory arthropathy caused by the deposition of monosodium urate (MSU) crystals. The burden of gout is substantial with increasing prevalence of gout globally. The prevalence of Gout in the United States has increased by over 7% in the last two decades. Initially, it was believed that MSU crystal deposits occur only in the joints with the involvement of the periarticular soft tissues, but recent studies have shown the presence of MSU crystal deposition in extra-articular sites as well. Human plasma becomes supersaturated with uric acid at 6.8 mg/dl, a state called hyperuricemia. Beyond this level, uric acid crystals precipitate out of the plasma and deposit in soft tissues, joints, kidneys, etc. If left untreated, hyperuricemia leads to chronic gout characterized by the deposition of tophi in soft tissues such as the joints, tendons, and bursae. With the advent of newer imaging techniques such as DECT, MSU crystals can be visualized in various extra-articular sites. Extra-articular deposition of MSU crystals is believed to be the causative factor for the development of multiple comorbidities in gout patients. Here, we review the literature on extra-articular deposition of urate crystals and the role of dual-energy computed tomography (DECT) in elucidating multi-organ involvement. DECT has emerged as an invaluable alternative for accurate and efficient MSU crystal deposition detection. Future studies using DECT can help determine the clinical consequences of extra-articular deposition of MSU in gout patients.

Clinical Applications of Dual-Energy CT

Dual-energy CT (DECT) provides insights into the material properties of tissues and can differentiate between tissues with similar attenuation on conventional single-energy imaging. In the conventional CT scanner, differences in the X-ray attenuation between adjacent structures are dependent on the atomic number of the materials involved, whereas in DECT, the difference in the attenuation is dependent on both the atomic number and electron density. The basic principle of DECT is to obtain two datasets with different X-ray energy levels from the same anatomic region and material decomposition based on attenuation differences at different energy levels. In this article, we discuss the clinical applications of DECT and its potential robust improvements in performance and postprocessing capabilities.

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with aχ McNema r ' s statistic 2 = 163 . 2 and a p-value of 2.23 × 10-37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

RSNA Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19: Interobserver Agreement Between Chest Radiologists

PURPOSE

To assess the interobserver variability between chest radiologists in the interpretation of the Radiological Society of North America (RSNA) expert consensus statement reporting guidelines in patients with suspected coronavirus disease 2019 (COVID-19) pneumonia in a setting with limited reverse transcription polymerase chain reaction testing availability.

METHODS

Chest computed tomography (CT) studies in 303 consecutive patients with suspected COVID-19 were reviewed by 3 fellowship-trained chest radiologists. Cases were assigned an impression of typical, indeterminate, atypical, or negative for COVID-19 pneumonia according to the RSNA expert consensus statement reporting guidelines, and interobserver analysis was performed. Objective CT features associated with COVID-19 pneumonia and distribution of findings were recorded.

RESULTS

The Fleiss kappa for all observers was almost perfect for typical (0.815), atypical (0.806), and negative (0.962) COVID-19 appearances (P < .0001) and substantial (0.636) for indeterminate COVID-19 appearance (P < .0001). Using Cramer V analysis, there were very strong correlations between all radiologists' interpretations, statistically significant for all (typical, indeterminate, atypical, and negative) COVID-19 appearances (P < .001). Objective CT imaging findings were recorded in similar percentages of typical cases by all observers.

CONCLUSION

The RSNA expert consensus statement on reporting chest CT findings related to COVID-19 demonstrates substantial to almost perfect interobserver agreement among chest radiologists in a relatively large cohort of patients with clinically suspected COVID-19. It therefore serves as a reliable reference framework for radiologists to accurately communicate their level of suspicion based on the presence of evidence-based objective findings.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

PURPOSE

To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

MATERIALS AND METHODS

In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobewise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April, 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.

RESULTS

Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO(P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.

CONCLUSION

A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

Radiological Society of North America (RSNA) Expert Consensus Statement Related to Chest CT Findings in COVID-19 Versus CO-RADS: Comparison of Reporting System Performance Among Chest Radiologists and End-User Preference

PURPOSE

The RSNA expert consensus statement and CO-RADS reporting system assist radiologists in describing lung imaging findings in a standardized manner in patients under investigation for COVID-19 pneumonia and provide clarity in communication with other healthcare providers. We aim to compare diagnostic performance and inter-/intra-observer among chest radiologists in the interpretation of RSNA and CO-RADS reporting systems and assess clinician preference.

METHODS

Chest CT scans of 279 patients with suspected COVID-19 who underwent RT-PCR testing were retrospectively and independently examined by 3 chest radiologists who assigned interpretation according to the RSNA and CO-RADS reporting systems. Inter-/intra-observer analysis was performed. Diagnostic accuracy of both reporting systems was calculated. 60 clinicians participated in a survey to assess end-user preference of the reporting systems.

RESULTS

Both systems demonstrated almost perfect inter-observer agreement (Fleiss kappa 0.871, P < 0.0001 for RSNA; 0.876, P < 0.0001 for CO-RADS impressions). Intra-observer agreement between the 2 scoring systems using the equivalent categories was almost perfect (Fleiss kappa 0.90-0.92, P < 0.001). Positive predictive values were high, 0.798-0.818 for RSNA and 0.891-0.903 CO-RADS. Negative predictive value were similar, 0.573-0.585 for RSNA and 0.573-0.58 for CO-RADS. Specificity differed between the 2 systems, 68-73% for CO-RADS and 52-58% for RSNA with superior specificity of CO-RADS. Of 60 survey participants, the majority preferred the RSNA reporting system rather than CO-RADS for all options provided (66.7-76.7%; P < 0.05).

CONCLUSIONS

RSNA and CO-RADS reporting systems are consistent and reproducible with near perfect inter-/intra-observer agreement and excellent positive predictive value. End-users preferred the reporting language in the RSNA system.

Virtual Dissection with Clinical Radiology Cases Provides Educational Value to First Year Medical Students

RATIONALE AND AIM

In virtual dissection, three-dimensional computed tomography scans are viewed on a near-life size virtual dissection table and through touchscreen technology, students work together to manipulate the data to perform their dissection. The purpose of this study was to develop a Virtual Dissection Curriculum for first year medical students and to assess its educational value as well as students' preferred pedagogy for learning with this new technology.

METHODS

One hundred and five first-year medical students participated in a case-based virtual dissection curriculum and were invited to complete a theory-based post experience survey. Eight unique clinical cases were selected based on the first-year curricular objectives and divided into four 30-minute sessions. In groups of 6-8, students reviewed the cases with a radiologist. First, students' reactions to virtual dissection were measured by three constructs using a 5-point Likert scale: quality of curriculum design (11 questions), impact on learning (7 questions), and comfort with technology (3 questions). Second, students ranked the usefulness of six pedagogical approaches for this technology. Responses were tabulated and rank order item lists were generated statistically using the Schulze method where appropriate.

RESULTS

The survey response rate was 83% (87/105). Overall, students' reactions to virtual dissection were positive across all three measured constructs. Most students indicated that the cases were of an appropriate level of difficulty (90%) and that virtual dissection improved their understanding of disease and pathology (89%), the clinical relevance of anatomy (77%), and visuospatial relationships (64%). Almost all students (94%) reported that the curriculum improved understanding of the role of the radiologist in patient care. Students felt that the "very useful" pedagogical approaches were small group demonstration (68%) and problem-based learning (51%).

CONCLUSION

First-year medical students perceive the use of virtual dissection as a valuable tool for learning anatomy and radiology. This technology enables the integration of clinical cases and radiology content into preclinical learning.

Gender Disparity in Radiology Society Committees and Leadership in North America and Comparison With Other Continents

PURPOSE

To evaluate gender distribution in radiology professional society leadership positions. Our study intends to assess and compare the gender distribution among leadership roles and professional society committee memberships of the radiology societies and seek an understanding of potential associations between gender, academic research metrics, institutional academic rank, and leadership roles.

METHODS

We identified radiology professional society committee members to assess relative gender composition in 28 radiology societies in North America, Europe, and Australia/New Zealand. The research metrics were obtained from the SCOPUS database and demographics and institutional affiliation through institutional websites' internet searches. Gender distribution by academic ranks and other discontinuous variables were analyzed using the Chi-Square test. Wallis tests.

RESULTS

Of the 3011 members of society committees, 67.9% were male, and 32.1% female. Among all the society members, the data showed that the proportion of committee members holding leadership positions was comparable between males (25.7%) and females (22.5%). However, when we did a subgroup analysis and disaggregated the data by leadership positions, we noted that among those who held the leadership positions, the proportion of males was more significant (n = 526, 70.7%) compared to females (n = 218, 29.3%). Overall, males had higher median publications, citations, H-indices, and active years of research (P< 0.0001). At all university academic ranks, men outnumbered females (P = 0.0015, Chi-square 15.38), with the most considerable disparity at the rank of professor (71.9% male, 28.1% female, P = 0.0003).

CONCLUSION

There was male predominance amongst committee members in radiology societies. Our study found no significant differences between those in leadership positions, suggesting that once a member of a committee, females are equally likely as males to attain leadership positions. Analysis of committee members' academic rank and committee leaders demonstrated underrepresentation of females at higher academic ranks, and males overall had higher research metrics than females.

Spectral Computed Tomography: Fundamental Principles and Recent Developments

CT is a diagnostic tool with many clinical applications. The CT voxel intensity is related to the magnitude of X-ray attenuation, which is not unique to a given material. Substances with different chemical compositions can be represented by similar voxel intensities, making the classification of different tissue types challenging. Compared to the conventional single-energy CT, spectral CT is an emerging technology offering superior material differentiation, which is achieved using the energy dependence of X-ray attenuation in any material. A specific form of spectral CT is dual-energy imaging, in which an additional X-ray attenuation measurement is obtained at a second X-ray energy. Dual-energy CT has been implemented in clinical settings with great success. This paper reviews the theoretical basis and practical implementation of spectral/dual-energy CT.

The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias

Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.

Specific manifestations of knee osteoarthritis predict depression and anxiety years in the future: Vancouver Longitudinal Study of Early Knee Osteoarthritis

Background

To evaluate whether knee osteoarthritis (OA) manifestations predict depression and anxiety using cross-sectional and longitudinal prediction models.

Methods

A population-based cohort (n = 122) with knee pain, aged 40–79, was evaluated at baseline, 3 and 7 years. Baseline predictors were: age decade; sex; BMI ≥ 25; physical exam knee effusion; crepitus; malalignment; quadriceps atrophy; flexion; flexion contracture; Kellgren-Lawrence (KL) x-ray grade (0/1/2/3+); WOMAC pain ≥25; WOMAC stiffness ≥25; self-reported knee swelling; and knee OA diagnosis (no/probable/definite). Depression and anxiety, cutoffs 5+ and 7+ respectively, were measured via the Hospital Anxiety and Depression Scale. We fit logistic models at each cycle using multivariable models selected via lowest Akaike’s information criterion.

Results

Baseline depression model: sex (female OR = 0.27; 0.10, 0.76) and KL grade (KL 1 OR = 4.21; 1.31, 13.48). Three-year depression model: KL grade (KL 1 OR = 18.92; 1.73, 206.25). Seven-year depression model: WOMAC stiffness ≥25 (OR = 3.49; 1.02, 11.94) and flexion contracture ≥1 degree (OR = 0.23; 0.07, 0.81). Baseline anxiety model: knee swelling (OR = 4.11; 1.51, 11.13) and age (50–59 vs. 40–49 OR = 0.31 [0.11, 0.85]; 60–69 OR = 0.07 [0.01, 0.42]). Three-year anxiety model: WOMAC stiffness ≥25 (OR = 5.80; 1.23, 27.29) and KL grade (KL 1 OR = 6.25; 1.04, 37.65). Seven-year anxiety model: sex (female OR = 2.71; 0.87, 8.46).

Conclusion

Specific knee OA-related manifestations predict depression and anxiety cross-sectionally, 3 years in the future, and for depression, 7 years in the future. This information may prove useful to clinicians in helping to identify patients most at risk of present or future depression and anxiety, thus facilitating preemptive discussions that may help counter that risk.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

PURPOSE

To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

MATERIALS AND METHODS

In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth.

RESULTS

Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO (P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.

CONCLUSION

A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

Canadian and American Emergency Radiology Fellowship Websites: An Evaluation of Content

PURPOSE

The internet is commonly employed by Radiology trainees to investigate and learn about potential fellowship programs. As a new and emerging subspecialty, Emergency Radiology requires strong internet presence and training program website content. This is vital to ensure good exposure of the fellowship programs to inform medical students, radiology trainees, and program directors, highlight unique aspects of a fellowship and raise awareness of the discipline at large.

METHODS

To assess the standard and depth of information available online, Canadian and American Radiology fellowship websites were evaluated for content. Thirty-six criteria related to application process and recruitment, departmental structure, incentives, education, and research and clinical training were evaluated for presence or absence.

RESULTS

Sixteen Emergency Radiology fellowship program websites were assessed from the United States and Canada for 36 criteria across 5 individual areas; application process and recruitment, departmental structure, incentives, education and research, and clinical training. Overall there was an absence of information found across all 5 areas. In particular areas for improvement were identified in education and research, and incentives both with median values of 12.5% of criteria present.

CONCLUSION

Most Emergency Radiology fellowship program websites demonstrate several information deficiencies. This relative lack of comprehensive information represents an actionable opportunity for individual programs and the field to better educate trainees, program directors and the public about the unique training of Emergency Radiologists.

Emergency Radiology During the COVID-19 Pandemic: The Canadian Association of Radiologists Recommendations for Practice

Coronavirus Disease 2019 (COVID-19) is the disease caused by the novel coronavirus officially named the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), declared as a pandemic by the World Health Organization on March 11, 2020. The COVID-19 pandemic presents an unprecedented challenge to emergency radiology practice. The continuity of an effective emergency imaging service for both COVID-19 and non-COVID-19 patients is essential, while adhering to best infection control practices. Under the direction of the Board of the Canadian Association of Radiologists, this general guidance document has been synthesized by collaborative consensus of a group of emergency radiologists. These recommendations aim to assist radiologists involved in emergency diagnostic imaging to help mitigate the spread of COVID-19 and continue to add value to patient care in the emergency setting.

Lower Urinary Tract Injuries: A Guide for the Emergency Radiologist

Traumatic lower urinary tract injuries are uncommon and mainly occur in patients with severe trauma and multiple abdominopelvic injuries. In the presence of other substantial injuries, bladder and urethral injuries may be overlooked and cause significant morbidity and mortality. Therefore, it is important that radiologists are familiar with mechanisms and injuries that are high risk for bladder and urethral trauma. We review the imaging findings associated with these injuries and the appropriate modalities and techniques to further evaluate the patient and accurately diagnose these injuries. Computed tomography cystography and conventional retrograde urethrography are effective tools in identifying injuries to the lower urinary tract and play a crucial role in patient care and prognosis.

Interventional radiology and COVID-19: evidence-based measures to limit transmission

As we face an explosion of COVID-19 cases and deal with an unprecedented set of circumstances all over the world, healthcare personnel are at the forefront, dealing with this emerging scenario. Certain subspecialties like interventional radiology entails a greater risk of acquiring and transmitting infection due to the close patient contact and invasive patient care the service provides. This makes it imperative to develop and set guidelines in place to limit transmission and utilize resources in an optimal fashion. A multi-tiered approach needs to be devised and monitored at the administrative level, taking into account the various staff and patient contact points. Based on these factors, work site and health force rearrangements need to be in place while enforcing segregation and disinfection parameters. We are putting forth an all-encompassing review of infection control measures that cover the dynamics of patient care and staff protocols that such a situation demands of an interventional department.

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department

Emergency and trauma radiologists, emergency department’s physicians and nurses, researchers, departmental leaders, and health policymakers have attempted to discover efficient approaches to enhance the provision of quality patient care. There are increasing expectations for radiology practices to deliver a dedicated emergency radiology service providing 24/7/365 on-site attending radiologist coverage. Emergency radiologists (ERs) are pressed to meet the demand of increased imaging volume, provide accurate reports, maintain a lower proportion of discrepancy rate, and with a rapid report turnaround time of finalized reports. Thus, rendering the radiologists overburdened. The demand for an increased efficiency in providing quality care to acute patients has led to the emergence of artificial intelligence (AI) in the field. AI can be used to assist emergency and trauma radiologists deal with the ever-increasing imaging volume and workload, as AI methods have typically demonstrated a variety of applications in medical image analysis and interpretation, albeit most programs are in a training or validation phase. This article aims to offer an evidence-based discourse about the evolving role of artificial intelligence in assisting the imaging pathway in an emergency and trauma radiology department. We hope to generate a multidisciplinary discourse that addresses the technical processes, the challenges in the labour-intensive process of training, validation and testing of an algorithm, the need for emphasis on ethics, and how an emergency radiologist’s role is pivotal in the execution of AI-guided systems within the context of an emergency and trauma radiology department. This exploratory narrative serves the present-day health leadership’s information needs by proposing an AI supported and radiologist centered framework depicting the work flow within a department. It is suspected that the use of such a framework, if efficacious, could provide considerable benefits for patient safety and quality of care provided. Additionally, alleviating radiologist burnout and decreasing healthcare costs over time.