Physician Burnout in Radiology: Perspectives From the Field.

Physician burnout is increasingly recognized as a public health crisis given the impact of burnout on physicians, their families, patients, communities, and population health. The COVID-19 pandemic has superimposed a new set of challenges for physicians to navigate, including unique challenges presented to radiologists. Radiologists from a diversity of backgrounds, practice settings, and career stages were asked for their perspectives on burnout.

Impact of the COVID-19 pandemic on the selection of chest imaging modalities and reporting systems: a survey of Italian radiologists.

PURPOSE: Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic. MATERIAL AND METHODS: The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM). RESULTS: The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems. CONCLUSION: This survey shows that the participants' responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.

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

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

Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19.

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.

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.

Impact of COVID-19 on radiology training: Royal College of Radiologists Junior Radiologists Forum national survey.

AIM: To obtain a national snapshot of radiology trainees' experience during the first wave of the pandemic. MATERIALS AND METHODS: A 25-item questionnaire was disseminated to representatives from all training regions across the UK in July 2020. Each representative collated the collective experiences of trainees in their training programme in key domains, including redeployment, shielding, training, and teaching. RESULTS: Ninety-five percent (38 of 40) of representatives completed the questionnaire. Trainees in up to 76% of training programmes were redeployed to wards and some trainees were shielding in 81% of programmes. Only 27% of programmes enabled remote reporting for isolating or shielding trainees. Sixty-two percent of respondents felt their well-being needs were supported. There was an overall increase in the attendance, volume, and quality of teaching and training nationally due to improved accessibility via remote-learning methods. Significant challenges were described with reporting, interventional procedures, and multidisciplinary team meeting attendance, although 62% of programmes noted an increase in service provision. Less in-person feedback was reported with in-person training still deemed necessary for practical skills. The Royal College of Radiologists Junior Radiologists Forum webinars were well received by all trainees with continuation of the series recommended. CONCLUSION: The COVID-19 pandemic has had a clear impact on many areas of radiology training in the UK. Early strategies have been adopted to mitigate the challenges faced by trainees and opportunities for future improvement are highlighted.

Short report: factors determining perceived stress among medical staff in radiology departments during the COVID-19 outbreak.

Medical staff in radiology departments faces a higher risk of infection and a heavier workload during the new coronavirus disease (COVID-19) outbreak. High perceived stress levels endanger physical and mental health and affect work efficiency and patient safety. Therefore, it is urgent to understand the perceived stress levels of medical staff and explore its risk factors. We recruited 600 medical staff from the radiology departments of 32 public hospitals in Sichuan Province, China, to evaluate perceived stress scores via a mobile app-based questionnaire. The results showed that the perceived stress level among medical staff in the radiology departments during the COVID-19 outbreak was high and a sense of tension was strongly present. A positive correlation was found between anxiety score and perceived stress. Multivariate analysis showed that risk factors for perceived stress were female, existing anxiety, and fears of being infected at work, an uncontrollable outbreak, and not being able to pay rent or mortgage. Conversely, good knowledge about COVID-19, being unmarried, and working in a higher-grade hospital were protective factors for perceived stress. Therefore, more attention should be given to medical staff in the radiology departments that present the risk factors outlined above. Timely risk assessment of psychological stress and effective intervention measures should be taken for these high-risk groups to keep their perceived stress within normal limits.

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. PURPOSE: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. METHODS: For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infection cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. RESULTS: Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. CONCLUSION: A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

Socio-economic and psychological impact of the COVID-19 outbreak on private practice and public hospital radiologists.

PURPOSE: The COVID-19 pandemic has led to an urgent reorganisation of the healthcare system to prevent hospitals from overflowing and the virus from spreading. Our objective was to evaluate the socioeconomic and psychological impact of the COVID-19 outbreak on radiologists. MATERIAL AND METHODS: French radiologists were invited to answer an online survey during the pandemic through mailing lists. The questionnaire was accessible for nine days. It covered socio-demographic information, exposure to COVID-19 at work and impact on work organisation, and included the Insomnia Severity Index and Hospital Anxiety and Depression Scale. Outcomes were moderate to severe insomnia, definite symptoms of depression or anxiety. Risk and protective factors were identified through multivariate binary logistic regression. RESULTS: 1515 radiologists answered the survey. Overall, 674 (44.5 %) worked in a highCOVID-19 density area, 671 (44.3 %) were women, and 809 (53.4 %) worked in private practice. Among responders, 186 (12.3 %) expressed insomnia, 222 (14.6 %) anxiety, and 189 (12.5 %) depression symptoms. Lack of protective equipment, increased teleradiology activity and negative impact on education were risk factors for insomnia (respectively OR [95 %CI]:1.7[1.1-2.7], 1.5[1.1-2.2], and 2.5[1.8-3.6]). Female gender, respiratory history, working in COVID-19 high density area, increase of COVID-19 related activity, and impacted education were risk factors for anxiety (OR[95 %CI]:1.7[1.2-2.3], 2[1.1-3.4], 1.5[1.1-2], 1.2[1-1.4], and 2.1[1.5-3]). Conversely, working in a public hospital was a protective factor against insomnia, anxiety, and depression (OR[95 %CI]:0.4[0.2-0.7], 0.6[0.4-0.9], and 0.5[0.3-0.8]). CONCLUSIONS: During COVID-19 pandemic, many radiologists expressed depression, anxiety and insomnia symptoms. Working in a public hospital was a protective factor against every psychological symptom. Socio-economic impact was also major especially in private practice.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Impact of Coronavirus Disease 2019 (COVID-19) on the Practice of Clinical Radiology.

The speed at which coronavirus disease 2019 (COVID-19) spread quickly fractured the radiology practice model in ways that were never considered. In March 2020, most practices saw an unprecedented drop in their volume of greater than 50%. The profound changes that have interrupted the arc of the radiology narrative may substantially dictate how health care and radiology services are delivered in the future. We examine the impact of COVID-19 on the future of radiology practice across the following domains: employment, compensation, and practice structure; location and hours of work; workplace environment and safety; activities beyond the "usual scope" of radiology practice; and CME, national meetings, and professional organizations. Our purpose is to share ideas that can help inform adaptive planning.

Impact of coronavirus disease 2019 (COVID-19) emergency on Italian radiologists: a national survey.

OBJECTIVES: To perform an online survey aimed at evaluating the impact of COVID-19 on Italian radiology departments. METHODS: We launched a survey composed of 25 questions about how COVID-19 has changed the safety and organization of daily activity in Italian radiology units. RESULTS: A total of 2136/10,564 (20.2%) radiologists of the Italian Society of Medical and Interventional Radiology participated. Two-thirds performed at least one diagnostic/interventional procedure on COVID-19 patients. The 88.1% reported a reduction in the elective imaging volumes, with US, mammography, and MRI having shown the greater decrease (41.1%, 23.9%, and 21.1%, respectively). In 69.6% of cases, institutions had trouble getting personal protective equipment (PPE), especially public hospitals and southern institutions. Less than 30% of participants were subjected to RT-PCR swab test, although 81.5% believed that it should be done on all health workers and 70% suggested it as the most important measure to improve safety at work. Slightly more than half of participants declared to work safely and felt to be adequately protected by their institutions. Up to 20% of northern participants were redeployed to clinical services. The first imaging examination performed by admitted COVID-19 patients was chest radiography in 76.3% of cases. Almost half of participants reported that less than 30% of health workers were infected in their radiology department, with higher rates in northern regions and public institutions. CONCLUSIONS: This snapshot of the current situation in Italian radiology departments could be used to harmonize the organization of working activity in order to safely and effectively face this pandemic. KEY POINTS: • More than two-thirds of institutions had trouble getting PPE for health workers, with public hospitals and southern institutions that presented more procurement problems • A substantial drop of imaging volumes was observed in the vast majority of Italian radiology departments, mostly due to the decrease of ultrasound, mammography, and MRI, especially in private practice were working activity was stopped in 13.3% of institutions • RT-PCR swab to health workers was reported as the most suggested measure by Italian radiologists to improve safety at work, as more than 80% of them believed that it should be performed to all health workers, although less than 30% were subjected to this test.

COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging.

PURPOSE: Because of the widespread use of CT in the diagnosis of COVID 19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way. METHODS: Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID 19. The patients' history, physical examination, CT findings, RT PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID 19 or non-COVID 19. Chest CT scans were classified according to the COVID 19 S CT diagnosis criteria. Cohen's kappa analysis was used. RESULTS: Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID 19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID 19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV. CONCLUSION: COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.