Industry Payments to Radiologists During the Last 5 Years and Impact of COVID-19 Pandemic.

OBJECTIVE: The aim of this study is to assess the trends in industry payments to radiologists and the impact of the COVID-19 pandemic, including trends in different categories of payments. METHODS: The Open Payments Database from CMS was accessed and analyzed for the period from January 1, 2016, to December 31, 2021. Payments were grouped into six categories: consulting fees, education, gifts, research, speaker fees, and royalties or ownership. The total number, value, and types of industry payments to radiologists were subsequently determined and compared pre- and postpandemic from 2016 to 2021. RESULTS: The total number of industry payments and the number of radiologists receiving these payments dropped by 50% and 32%, respectively, between 2019 and 2020, with only partial recovery in 2021. However, the mean payment value and total payment value increased by 177% and 37%, respectively, between 2019 and 2020. Gifts and speaker fees experienced the largest decreases between 2019 and 2020 (54% and 63%, respectively). Research and education grants were also disrupted, with the number of payments decreasing by 37% and 36% and payment value decreasing by 37% and 25%, respectively. However, royalty or ownership increased during the first year of the pandemic (8% for number of payments and 345% for value of payments). CONCLUSIONS: There was significant decline in overall industry payments coinciding with the COVID-19 pandemic, with biggest declines in gifts and speaker fees. The impact on the different categories of payments and recovery in the last 2 years has been heterogeneous.

Medicaid and the Children's Health Insurance Program: an overview for the pediatric radiologist.

In terms of number of beneficiaries, Medicaid is the single largest health insurance program in the US. Along with the Children's Health Insurance Program (CHIP), Medicaid covers nearly half of all births and provides health insurance to nearly half of the children in the country. This article provides a broad introduction to Medicaid and CHIP for the pediatric radiologist with a special focus on topics relevant to pediatric imaging and population health. This includes an overview of Medicaid's structure and eligibility criteria and how it differs from Medicare. The paper examines the means-tested programs within the context of pediatric radiology, reviewing pertinent topics such as the rise of Medicaid managed care plans, Medicaid expansion, the effects of Medicaid on child health, and COVID-19. Beyond the basics of benefits coverage, pediatric radiologists should understand how Medicaid and CHIP financing and reimbursement affect the ability of pediatric practices, radiology groups, and hospitals to provide services for children in a sustainable manner. The paper concludes with an analysis of future opportunities for Medicaid and CHIP.

Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.

PROBLEM: Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. AIM: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. METHODS: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. RESULTS: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. CONCLUSION: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Impacts of the COVID-19 pandemic on the routine of Brazilian oral radiologists.

OBJECTIVE: To analyze the challenges and impacts of COVID-19 on the routine of Brazilian oral radiologists regarding changes in biosafety protocols, number of patients and staff, the flow of acquisition, and availability of images. METHODS: Structured digital questionnaires with questions related to the impacts of the COVID-19 pandemic on Oral Radiology were applied and analyzed. Descriptive statistical analysis was used to describe the items included in the survey, and means and standard deviations were calculated to describe continuous variables and frequency percentages to describe categorical data. RESULTS: A high number of Brazilian oral radiologists continued to work in the pandemic period, with little or no change in their working hours. Digital flow and teleradiology are in most of their workplaces and the changes imposed by the pandemic will be incorporated and permanent, according to most of the participants in this study. CONCLUSIONS: The COVID-19 pandemic brought important impacts on radiology clinics, with changes in the flow of patients, in the service and in the type of exam performed. In addition, adaptation to biosafety standards became necessary, with a significant increase in spending on personal protective equipment.

Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review.

Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.

Trends in Academic Productivity Among Radiologists During the COVID-19 Pandemic.

PURPOSE: There is a scarcity of literature examining changes in radiologist research productivity during the COVID-19 pandemic. The current study aimed to investigate changes in academic productivity as measured by publication volume before and during the COVID-19 pandemic. METHODS: This single-center, retrospective cohort study included the publication data of 216 researchers consisting of associate professors, assistant professors, and professors of radiology. Wilcoxon's signed-rank test was used to identify changes in publication volume between the 1-year-long defined prepandemic period (publications between May 1, 2019, and April 30, 2020) and COVID-19 pandemic period (May 1, 2020, to April 30, 2021). RESULTS: There was a significantly increased mean annual volume of publications in the pandemic period (5.98, SD = 7.28) compared with the prepandemic period (4.98, SD = 5.53) (z = -2.819, P = .005). Subset analysis demonstrated a similar (17.4%) increase in publication volume for male researchers when comparing the mean annual prepandemic publications (5.10, SD = 5.79) compared with the pandemic period (5.99, SD = 7.60) (z = -2.369, P = .018). No statistically significant changes were found in similar analyses with the female subset. DISCUSSION: Significant increases in radiologist publication volume were found during the COVID-19 pandemic compared with the year before. Changes may reflect an overall increase in academic productivity in response to clinical and imaging volume ramp down.

The role of teleradiology during COVID-19 outbreak: Saudi radiologists' perspectives.

OBJECTIVES: To evaluate the role of teleradiology during the COVID-19 pandemic from Saudi radiologists' perspectives to improve the radiology quality service. METHODS: A cross-sectional study was carried out in Saudi Arabia among radiologists working at local hospitals from October to November 2021. It contains 21 questions involved demographic information; general information on teleradiology services; and the impact of teleradiology during COVID-19. One-way ANOVA was used to compare demographic groups. Chi-square test was used to compare demographic groups regarding their distribution of responses. All tests were carried out <0.05 level of significance. RESULTS: A total of 102 radiologists participated in this study (56% males, 44% females), 58.8% of them were sub-specialized in chest radiology. Regarding the general status of teleradiology, 69.6% of participants believed that teleradiology is a helpful tool for imaging interpretation. However, 44% of them were uncertain on the impact of teleradiology on patients' confidentiality. Approximately 87% of participants agreed that there is a positive contribution of teleradiology during COVID-19, which enables decreasing risk of infection and workload. There was a significant difference between professional degrees and overall participant responses (p<0.05). Academicians agreed that it enhances radiology departments' work (mean=17.78, SD=1.86). CONCLUSION: Concerns raised on complicated cases that require physical presence of patients, cannot be performed by teleradiology. Additionally, it might provide insufficient communication with other professionals to discuss images.

An Integrated Radiologic-Pathologic Understanding of COVID-19 Pneumonia.

This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the fundamental nature of the disease. The radiologic and pathologic manifestations of COVID-19 pneumonia result from epithelial and endothelial injuries based on viral toxicity and immunopathologic effects. The pathologic features of mild and reversible COVID-19 pneumonia involve nonspecific pneumonia or an organizing pneumonia pattern, while the pathologic features of potentially fatal and irreversible COVID-19 pneumonia are characterized by diffuse alveolar damage followed by fibrosis or acute fibrinous organizing pneumonia. These pathologic responses of epithelial injuries observed in COVID-19 pneumonia are not specific to SARS-CoV-2 but rather constitute universal responses to viral pneumonia. Endothelial injury in COVID-19 pneumonia is a prominent feature compared with other types of viral pneumonia and encompasses various vascular abnormalities at different levels, including pulmonary thromboembolism, vascular engorgement, peripheral vascular reduction, a vascular tree-in-bud pattern, and lung perfusion abnormality. Chest CT with different imaging techniques (eg, CT quantification, dual-energy CT perfusion) can fully capture the various manifestations of epithelial and endothelial injuries. CT can thus aid in establishing prognosis and identifying patients at risk for deterioration.

Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees.

INTRODUCTION: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. METHODOLOGY: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. RESULTS: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. CONCLUSIONS: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. ADVANCES IN KNOWLEDGE: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases.

Coronavirus disease 2019 (COVID-19) is associated with pneumonia and has various pulmonary manifestations on computed tomography (CT). Although COVID-19 pneumonia is usually seen as bilateral predominantly peripheral ground-glass opacities with or without consolidation, it can present with atypical radiological findings and resemble the imaging findings of other lung diseases. Diagnosis of COVID-19 pneumonia is much more challenging for both clinicians and radiologists in the presence of pre-existing lung disease. The imaging features of COVID-19 and underlying lung disease can overlap and obscure the findings of each other. Knowledge of the radiological findings of both diseases and possible complications, correct diagnosis, and multidisciplinary consensus play key roles in the appropriate management of diseases. In this pictorial review, the chest CT findings are presented of patients with underlying lung diseases and overlapping COVID-19 pneumonia and the various reasons for radiological lung abnormalities in these patients are discussed.

Spectrum of opportunistic fungal lung co-infections in COVID-19: What the radiologist needs to know.

Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.

Axillary Lymphadenopathy in the COVID-19 Era: What the Radiologist Needs to Know.

Axillary lymphadenopathy caused by the high immunogenicity of messenger RNA (mRNA) COVID-19 vaccines presents radiologists with new diagnostic dilemmas in differentiating vaccine-related benign reactive lymphadenopathy from that due to malignant causes. Understanding axillary anatomy and lymphatic drainage is key to radiologic evaluation of the axilla. US plays a critical role in evaluation and classification of axillary lymph nodes on the basis of their cortical and hilar morphology, which allows prediction of metastatic disease. Guidelines for evaluation and management of axillary lymphadenopathy continue to evolve as radiologists gain more experience with axillary lymphadenopathy related to COVID-19 vaccines. General guidelines recommend documenting vaccination dates and laterality and administering all vaccine doses contralateral to the site of primary malignancy whenever applicable. Guidelines also recommend against postponing imaging for urgent clinical indications or for treatment planning in patients with newly diagnosed breast cancer. Although conservative management approaches to axillary lymphadenopathy initially recommended universal short-interval imaging follow-up, updates to those approaches as well as risk-stratified approaches recommend interpreting lymphadenopathy in the context of both vaccination timing and the patient's overall risk of metastatic disease. Patients with active breast cancer in the pretreatment or peritreatment phase should be evaluated with standard imaging protocols regardless of vaccination status. Tissue sampling and multidisciplinary discussion remain useful in management of complex cases, including increasing lymphadenopathy at follow-up imaging, MRI evaluation of extent of disease, response to neoadjuvant treatment, and potentially confounding cases. An invited commentary by Weinstein is available online. ©RSNA, 2022.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.

Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient's metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely-direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The proposed framework helps clinicians diagnose lung inflammation and provide an end-to-end robust diagnostic system. Initially, the feasibility of integrating patients' symptoms, clinical pathologies, and radiologist feedback with CAD system to improve the classification performance is investigated. Subsequently, the patient's metadata and radiologist feedback are integrated with the CAD system using the proposed integration frameworks. The proposed method's performance is evaluated using a private dataset consisting of 70 chest X-ray (CXR) images (31 COVID-19, 14 other diseases, and 25 normal). The obtained results reveal that the proposed WBI achieved the highest classification performance (accuracy = 98.18%, F1 score = 97.73%, and Matthew's correlation coefficient = 0.969) compared to DI and RI. The generalization capability of the proposed framework is also verified from an external validation set. Furthermore, the Friedman average ranking and Shaffer and Holm post hoc statistical methods reveal the obtained results' statistical significance. Methodological diagram of proposed integration frameworks.

Mandating Limits on Workload, Duty, and Speed in Radiology.

Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.