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1.
Insights Imaging ; 13(1): 41, 2022 Mar 07.
Article in English | MEDLINE | ID: covidwho-1731541

ABSTRACT

OBJECTIVES: Data from radiological departments provide important information on overall quantities of medical care provided. With this study we used a comprehensive analysis of radiological examinations as a surrogate marker to quantify the effect of the different COVID-19 waves on medical care provided. METHODS: Radiological examination volumes during the different waves of infection were compared among each other as well as to time-matched control periods from pre-pandemic years using a locally weighted scatterplot smoothing as well as negative binominal regression models. RESULTS: A total of 1,321,119 radiological examinations were analyzed. Examination volumes were reduced by about 10% over the whole study period (IRR = 0.90; 95% CI 0.89-0.92), with a focus on acute medical care (0.84; 0.83-0.85) and outpatients (0.93: 0.90-0.97). When compared to wave 1, examination volumes were about 17% higher during wave 2 (1.17; 1.10-1.25), and 33% higher in wave 3 of the pandemic (1.33; 1.24-1.42). CONCLUSIONS: This study shows the severe effect of COVID-19 pandemic and related shutdown measures on overall provided medical care as measured by radiological examinations. When compared, the decrease of medical care was more pronounced in the earlier waves of the pandemic.

2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-291141

ABSTRACT

Segmentation of pulmonary infiltrates can help assess severity of COVID-19, but manual segmentation is labor and time-intensive. Using neural networks to segment pulmonary infiltrates would enable automation of this task. However, training a 3D U-Net from computed tomography (CT) data is time- and resource-intensive. In this work, we therefore developed and tested a solution on how transfer learning can be used to train state-of-the-art segmentation models on limited hardware and in shorter time. We use the recently published RSNA International COVID-19 Open Radiology Database (RICORD) to train a fully three-dimensional U-Net architecture using an 18-layer 3D ResNet, pretrained on the Kinetics-400 dataset as encoder. The generalization of the model was then tested on two openly available datasets of patients with COVID-19, who received chest CTs (Corona Cases and MosMed datasets). Our model performed comparable to previously published 3D U-Net architectures, achieving a mean Dice score of 0.679 on the tuning dataset, 0.648 on the Coronacases dataset and 0.405 on the MosMed dataset. Notably, these results were achieved with shorter training time on a single GPU with less memory available than the GPUs used in previous studies.

3.
Rofo ; 194(1): 70-82, 2022 01.
Article in English | MEDLINE | ID: covidwho-1467162

ABSTRACT

OBJECTIVES: To find out the opinion of radiological inpatient and outpatient medical staff regarding the measures taken in relation to the COVID-19 pandemic during the first and second waves and to identify the measures that are still perceived as needing improvement. MATERIALS AND METHODS: We conducted an anonymous online survey among more than 10 000 radiologists/technicians in Germany from January 5 to January 31, 2021. A total of 862 responses (head physicians, n = 225 [inpatient doctors, n = 138; outpatient doctors, n = 84; N/A, n = 3]; radiologic personnel, n = 637 [inpatient doctor, n = 303; outpatient doctor, n = 50; inpatient technician, n = 217; outpatient technician, n = 26; N/A, n = 41]) were received. Questions of approximation, yes/no questions, and Likert scales were used. RESULTS: During the first/second wave, 70 % (86/123)/43 % (45/104) of inpatient and 26 % (17/66)/10 % (5/52) of outpatient head physicians agreed that they received financial support from the authorities but the majority rated the financial support as insufficient. During the first and second wave, 33 % (8/24) and 80 % (16/20) of outpatient technicians agreed that they were adequately provided with personal protective equipment. The perceived lack of personal protective equipment improved for all participants during the second wave. Inpatient [outpatient] technicians perceived an increased workload in the first and second wave: 72 % (142/198) [79 % (19/24)] and 84 % (146/174) [80 % (16/20)]. CONCLUSION: Technicians seem increasingly negatively affected by the COVID-19 pandemic in Germany. Financial support by the competent authorities seems to be in need of improvement. KEY POINTS: · The accessibility of personal protective equipment resources improved in the second wave.. · In particular, radiology technicians seem increasingly negatively affected by the COVID-19 pandemic.. · Financial and consulting support from the government could be improved.. CITATION FORMAT: · Bernatz S, Afat S, Othman AE et al. Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave. Fortschr Röntgenstr 2022; 194: 70 - 82.


Subject(s)
COVID-19 , Radiology , Ambulatory Care , Germany , Humans , Inpatients , Pandemics , SARS-CoV-2
4.
Sci Rep ; 11(1): 10678, 2021 05 21.
Article in English | MEDLINE | ID: covidwho-1238016

ABSTRACT

With an urgent need for bedside imaging of coronavirus disease 2019 (COVID-19), this study's main goal was to assess inter- and intraobserver agreement in lung ultrasound (LUS) of COVID-19 patients. In this single-center study we prospectively acquired and evaluated 100 recorded ten-second cine-loops in confirmed COVID-19 intensive care unit (ICU) patients. All loops were rated by ten observers with different subspeciality backgrounds for four times by each observer (400 loops overall) in a random sequence using a web-based rating tool. We analyzed inter- and intraobserver variability for specific pathologies and a semiquantitative LUS score. Interobserver agreement for both, identification of specific pathologies and assignment of LUS scores was fair to moderate (e.g., LUS score 1 Fleiss' κ = 0.27; subpleural consolidations Fleiss' κ = 0.59). Intraobserver agreement was mostly moderate to substantial with generally higher agreement for more distinct findings (e.g., lowest LUS score 0 vs. highest LUS score 3 (median Fleiss' κ = 0.71 vs. 0.79) or air bronchograms (median Fleiss' κ = 0.72)). Intraobserver consistency was relatively low for intermediate LUS scores (e.g. LUS Score 1 median Fleiss' κ = 0.52). We therefore conclude that more distinct LUS findings (e.g., air bronchograms, subpleural consolidations) may be more suitable for disease monitoring, especially with more than one investigator and that training material used for LUS in point-of-care ultrasound (POCUS) should pay refined attention to areas such as B-line quantification and differentiation of intermediate LUS scores.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Point-of-Care Systems , SARS-CoV-2 , COVID-19/therapy , Female , Humans , Male , Middle Aged , Monitoring, Physiologic , Observer Variation , Prospective Studies , Ultrasonography
5.
NPJ Digit Med ; 4(1): 69, 2021 Apr 12.
Article in English | MEDLINE | ID: covidwho-1180281

ABSTRACT

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint LesionTM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

6.
Rofo ; 193(8): 937-946, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1139768

ABSTRACT

OBJECTIVES: As a cross-section discipline within the hospital infrastructure, radiological departments might be able to provide important information regarding the impact of the COVID-19 pandemic on healthcare. The goal of this study was to quantify changes in medical care during the first wave of the pandemic using radiological examinations as a comprehensive surrogate marker and to determine potential future workload. METHODS: A retrospective analysis of all radiological examinations during the first wave of the pandemic was performed. The number of examinations was compared to time-matched control periods. Furthermore, an in-depth analysis of radiological examinations attributed to various medical specialties was conducted and postponed examinations were extrapolated to calculate additional workload in the near future. RESULTS: A total of 596,760 examinations were analyzed. Overall case volumes decreased by an average of 41 % during the shutdown compared to the control period. The most affected radiological modalities were sonography (-54 %), X-ray (-47 %) followed by MRI (-42 %). The most affected medical specialty was trauma and orthopedics (-60 % case volume) followed by general surgery (-49 %). Examination numbers increased during the post-shutdown period leading to a predicted additional workload of up to 22 %. CONCLUSION: This study shows a marked decrease in radiological examinations in total and among several core medical specialties, indicating a significant reduction in medical care during the first COVID-19 shutdown. KEY POINTS: · Number of radiological examinations decreased by 41 % during the first wave of the COVID-19 pandemic.. · Several core medical specialties were heavily affected with a reduction of case volumes up to 60 %.. · When extrapolating postponed examinations to the near future, the overall workload for radiological departments might increase up to 22 %.. CITATION FORMAT: · Fleckenstein FN, Maleitzke T, Böning G et al. Decreased Medical Care During the COVID-19 Pandemic - A Comprehensive Analysis of Radiological Examinations. Fortschr Röntgenstr 2021; 193: 937 - 946.


Subject(s)
COVID-19 , Pandemics , Radiography , Radiology Department, Hospital , Radiology , Workload , Delivery of Health Care , Humans , Orthopedics , Radiography/trends , Radiology/trends , Retrospective Studies
7.
Metabolism ; 110: 154317, 2020 09.
Article in English | MEDLINE | ID: covidwho-935816

ABSTRACT

BACKGROUND AND AIMS: Overall obesity has recently been established as an independent risk factor for critical illness in patients with coronavirus disease 2019 (COVID-19). The role of fat distribution and especially that of visceral fat, which is often associated with metabolic syndrome, remains unclear. Therefore, this study aims at investigating the association between fat distribution and COVID-19 severity. METHODS: Thirty patients with COVID-19 and a mean age of 65.6 ±â€¯13.1 years from a level-one medical center in Berlin, Germany, were included in the present cross-sectional analysis. COVID-19 was confirmed by polymerase chain reaction (PCR) from nasal and throat swabs. A severe clinical course of COVID-19 was defined by hospitalization in the intensive care unit (ICU) and/or invasive mechanical ventilation. Fat was measured at the level of the first lumbar vertebra on routinely acquired low-dose chest computed tomography (CT). RESULTS: An increase in visceral fat area (VFA) by ten square centimeters was associated with a 1.37-fold higher likelihood of ICU treatment and a 1.32-fold higher likelihood of mechanical ventilation (adjusted for age and sex). For upper abdominal circumference, each additional centimeter of circumference was associated with a 1.13-fold higher likelihood of ICU treatment and a 1.25-fold higher likelihood of mechanical ventilation. CONCLUSIONS: Our proof-of-concept study suggests that visceral adipose tissue and upper abdominal circumference specifically increase the likelihood of COVID-19 severity. CT-based quantification of visceral adipose tissue and upper abdominal circumference in routine chest CTs may therefore be a simple tool for risk assessment in COVID-19 patients.


Subject(s)
Adiposity/physiology , Betacoronavirus , Coronavirus Infections/etiology , Intra-Abdominal Fat/physiology , Pneumonia, Viral/etiology , Aged , Aged, 80 and over , COVID-19 , Cross-Sectional Studies , Humans , Intra-Abdominal Fat/diagnostic imaging , Middle Aged , Pandemics , Pilot Projects , SARS-CoV-2 , Tomography, X-Ray Computed
8.
Diagnostics (Basel) ; 10(11)2020 Nov 10.
Article in English | MEDLINE | ID: covidwho-918181

ABSTRACT

Computed tomography (CT) plays an important role in the diagnosis of COVID-19. The aim of this study was to evaluate a simple, semi-quantitative method that can be used for identifying patients in need of subsequent intensive care unit (ICU) treatment and intubation. We retrospectively analyzed the initial CT scans of 28 patients who tested positive for SARS-CoV-2 at our Level-I center. The extent of lung involvement on CT was classified both subjectively and with a simple semi-quantitative method measuring the affected area at three lung levels. Competing risks Cox regression was used to identify factors associated with the time to ICU admission and intubation. Their potential diagnostic ability was assessed with receiver operating characteristic (ROC)/area under the ROC curves (AUC) analysis. A 10% increase in the affected lung parenchyma area increased the instantaneous risk of intubation (hazard ratio (HR) = 2.00) and the instantaneous risk of ICU admission (HR 1.73). The semi-quantitative measurement outperformed the subjective assessment diagnostic ability (AUC = 85.6% for ICU treatment, 71.9% for intubation). This simple measurement of the involved lung area in initial CT scans of COVID-19 patients may allow early identification of patients in need of ICU treatment/intubation and thus help make optimal use of limited ICU/ventilation resources in hospitals.

9.
Eur J Radiol Open ; 7: 100283, 2020.
Article in English | MEDLINE | ID: covidwho-898807

ABSTRACT

PURPOSE: Computed tomography (CT) is used for initial diagnosis and therapy monitoring of patients with coronavirus disease 2019 (COVID-19). As patients of all ages are affected, radiation dose is a concern. While follow-up CT examinations lead to high cumulative radiation doses, the ALARA principle states that the applied dose should be as low as possible while maintaining adequate image quality. The aim of this study was to evaluate parameter settings for two commonly used CT scanners to ensure sufficient image quality/diagnostic confidence at a submillisievert dose. MATERIALS AND METHODS: We retrospectively analyzed 36 proven COVID-19 cases examined on two different scanners. Image quality was evaluated objectively as signal-to-noise ratio (SNR)/contrast-to-noise ratio (CNR) measurement and subjectively by two experienced, independent readers using 3-point Likert scales. CT dose index volume (CTDIvol) and dose-length product (DLP) were extracted from dose reports, and effective dose was calculated. RESULTS: With the tested parameter settings we achieved effective doses below 1 mSv (median 0.5 mSv, IQR: 0.2 mSv, range: 0.3-0.9 mSv) in all 36 patients. Thirty-four patients had typical COVID-19 findings. Both readers were confident regarding the typical COVID-19 CT-characteristics in all cases (3 ± 0). Objective image quality parameters were: SNRnormal lung: 17.0 ± 5.9, CNRGGO/normal lung: 7.5 ± 5.0, and CNRconsolidation/normal lung: 15.3 ± 6.1. CONCLUSION: With the tested parameters, we achieved applied doses in the submillisievert range, on two different CT scanners without sacrificing diagnostic confidence regarding COVID-19 findings.

10.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-1650

ABSTRACT

Background: Profound evaluation on the impact of the COVID-19 pandemic and related political measures on healthcare infrastructure is scarce and only slowly eme

11.
Sci Rep ; 10(1): 13590, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-713031

ABSTRACT

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Deep Learning , Diagnosis, Computer-Assisted/methods , Neural Networks, Computer , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic/classification , Thorax/diagnostic imaging , COVID-19 , Coronavirus Infections/virology , Humans , Pandemics , Pneumonia, Viral/virology , ROC Curve , SARS-CoV-2 , Sensitivity and Specificity
12.
J Clin Med ; 9(8)2020 Aug 06.
Article in English | MEDLINE | ID: covidwho-711365

ABSTRACT

BACKGROUND AND PURPOSE: Intracranial hemorrhage has been observed in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19), but the clinical, imaging, and pathophysiological features of intracranial bleeding during COVID-19 infection remain poorly characterized. This study describes clinical and imaging characteristics of patients with COVID-19 infection who presented with intracranial bleeding in a European multicenter cohort. METHODS: This is a multicenter retrospective, observational case series including 18 consecutive patients with COVID-19 infection and intracranial hemorrhage. Data were collected from February to May 2020 at five designated European special care centers for COVID-19. The diagnosis of COVID-19 was based on laboratory-confirmed diagnosis of SARS-CoV-2. Intracranial bleeding was diagnosed on computed tomography (CT) of the brain within one month of the date of COVID-19 diagnosis. The clinical, laboratory, radiologic, and pathologic findings, therapy and outcomes in COVID-19 patients presenting with intracranial bleeding were analyzed. RESULTS: Eighteen patients had evidence of acute intracranial bleeding within 11 days (IQR 9-29) of admission. Six patients had parenchymal hemorrhage (33.3%), 11 had subarachnoid hemorrhage (SAH) (61.1%), and one patient had subdural hemorrhage (5.6%). Three patients presented with intraventricular hemorrhage (IVH) (16.7%). CONCLUSION: This study represents the largest case series of patients with intracranial hemorrhage diagnosed with COVID-19 based on key European countries with geospatial hotspots of SARS-CoV-2. Isolated SAH along the convexity may be a predominant bleeding manifestation and may occur in a late temporal course of severe COVID-19.

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