Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints.

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection. Purpose To create a deep neural network to detect MRI changes in sacroiliac joints indicative of axSpA. Materials and Methods This retrospective multicenter study included MRI examinations of five cohorts of patients with clinical suspicion of axSpA collected at university and community hospitals between January 2006 and September 2020. Data from four cohorts were used as the training set, and data from one cohort as the external test set. Each MRI examination in the training and test sets was scored by six and seven raters, respectively, for inflammatory changes (bone marrow edema, enthesitis) and structural changes (erosions, sclerosis). A deep learning tool to detect changes indicative of axSpA was developed. First, a neural network to homogenize the images, then a classification network were trained. Performance was evaluated with use of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. P < .05 was considered indicative of statistically significant difference. Results Overall, 593 patients (mean age, 37 years ± 11 [SD]; 302 women) were studied. Inflammatory and structural changes were found in 197 of 477 patients (41%) and 244 of 477 (51%), respectively, in the training set and 25 of 116 patients (22%) and 26 of 116 (22%) in the test set. The AUCs were 0.94 (95% CI: 0.84, 0.97) for all inflammatory changes, 0.88 (95% CI: 0.80, 0.95) for inflammatory changes fulfilling the Assessment of SpondyloArthritis international Society definition, and 0.89 (95% CI: 0.81, 0.96) for structural changes indicative of axSpA. Sensitivity and specificity on the external test set were 22 of 25 patients (88%) and 65 of 91 patients (71%), respectively, for inflammatory changes and 22 of 26 patients (85%) and 70 of 90 patients (78%) for structural changes. Conclusion Deep neural networks can detect inflammatory or structural changes to the sacroiliac joint indicative of axial spondyloarthritis at MRI. © RSNA, 2022 Online supplemental material is available for this article.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

MOTIVATION: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results. RESULTS: Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability and implementationWe make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

OBJECTIVE: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. MATERIALS AND METHODS: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. RESULTS: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. CONCLUSION: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.

Cooling Effects Occur in Hepatic Microwave Ablation At Low Vascular Flow Rates and in Close Proximity to Liver Vessels - Ex Vivo.

Background. The impact of vascular cooling effects in hepatic microwave ablation (MWA) is controversially discussed. The objective of this study was a systematic assessment of vascular cooling effects in hepatic MWA ex vivo. Methods. Microwave ablations were performed in fresh porcine liver ex vivo with a temperature-controlled MWA generator (902-928 MHz) and a non-cooled 14-G-antenna. Energy input was set to 9.0 kJ. Hepatic vessels were simulated by glass tubes. Three different vessel diameters (3.0, 5.0, 8.0 mm) and vessel to antenna distances (5, 10, 20 mm) were examined. Vessels were perfused with saline solution at nine different flow rates (0-500 mL/min). Vascular cooling effects were assessed at the largest cross-sectional ablation area. A quantitative and semi-quantitative/morphologic analysis was carried out. Results. 228 ablations were performed. Vascular cooling effects were observed at close (5 mm) and medium (10 mm) antenna to vessel distances (P < .05). Vascular cooling effects occurred around vessels with flow rates ≥1.0 mL/min (P < .05) and a vessel diameter ≥3 mm (P < .05). Higher flow rates did not result in more distinct cooling effects (P > .05). No cooling effects were measured at large (20 mm) antenna to vessel distances (P > .05). Conclusion. Vascular cooling effects occur in hepatic MWA and should be considered in treatment planning. The vascular cooling effect was mainly affected by antenna to vessel distance. Vessel diameter and vascular flow rate played a minor role in vascular cooling effects.

Periportal fields cause stronger cooling effects than veins in hepatic microwave ablation: an in vivo porcine study.

BACKGROUND: Vascular cooling effects are a well-known source for tumor recurrence in thermal in situ ablation techniques for hepatic malignancies. Microwave ablation (MWA) is an ablation technique to be considered in the treatment of malignant liver tumors. The impact of vascular cooling in MWA is still controversial. PURPOSE: To evaluate the influence of different intrahepatic vessel types, vessel sizes, and vessel-to-antenna-distances on MWA geometry in vivo. MATERIAL AND METHODS: Five MWAs (902-928 MHz) were performed with an energy input of 24.0 kJ in three porcine livers in vivo. MWA lesions were cut into 2-mm slices. The minimum and maximum radius of the ablation area was measured for each slice. Distances were measured from ablation center toward all adjacent hepatic vessels with a diameter of ≥1 mm and within a perimeter of 20 mm around the antenna. The respective vascular cooling effect relative to the maximum ablation radius was calculated. RESULTS: In total, 707 vessels (489 veins, 218 portal fields) were detected; 370 (76%) hepatic veins and 185 (85%) portal fields caused a cooling effect. Portal fields resulted in higher cooling effects (37%) than hepatic veins (26%, P < 0.01). No cooling effect could be observed in close proximity of vessels within the central ablation zone. CONCLUSION: Hepatic vessels influenced MWA zones and caused a distinct cooling effect. Portal fields resulted in more pronounced cooling effect than hepatic veins. No cooling effect was observed around vessels situated within the central white zone.

CT-based quantification of short-term tissue shrinkage following hepatic microwave ablation in an in vivo porcine liver model.

BACKGROUND: Microwave ablation (MWA) is a minimally invasive treatment option for solid tumors and belongs to the local ablative therapeutic techniques, based on thermal tissue coagulation. So far there are mainly ex vivo studies that describe tissue shrinkage during MWA. PURPOSE: To characterize short-term volume changes of the ablated zone following hepatic MWA in an in vivo porcine liver model using contrast-enhanced computer tomography (CECT). MATERIAL AND METHODS: We performed multiple hepatic MWA with constant energy parameters in healthy, narcotized and laparotomized domestic pigs. The volumes of the ablated areas were calculated from venous phase CT scans, immediately after the ablation and in short-term courses of up to 2 h after MWA. RESULTS: In total, 19 thermally ablated areas in 10 porcine livers could be analyzed (n = 6 with two volume measurements during the measurement period and n = 13 with three measurements). Both groups showed a statistically significant but heterogeneous volume reduction of up to 12% (median 6%) of the ablated zones in CECT scans during the measurement period (P < 0.001 [n = 13] and P = 0.042 [n = 6]). However, the dimension and dynamics of volume changes were heterogenous both absolutely and relatively. CONCLUSION: We observed a significant short-term volume reduction of ablated liver tissue in vivo. This volume shrinkage must be considered in clinical practice for technically successful tumor treatment by MWA and therefore it should be further investigated in in vivo studies.

Immediate post-interventional contrast-enhanced computed tomography overestimates hepatic microwave ablation - an in vivo animal study.

Objectives: Contrast-enhanced computed tomography (CECT) is used to monitor technical success immediately after hepatic microwave ablation (MWA). However, it remains unclear, if CECT shows the exact extend of the thermal destruction zone, or if tissue changes such as peri-lesionary edema are depicted as well. The objective of this study was to correlate immediate post-interventional CECT with histological and macroscopic findings in hepatic MWA in porcine liver in vivo.Methods: Eleven MWA were performed in porcine liver in vivo with a microwave generator (928 MHz; energy input 24 kJ). CECT was performed post-interventionally. Livers were explanted and ablations were bisected immediately after ablation. Samples were histologically analyzed after vital staining (NADH-diaphorase). Ablation zones were histologically and macroscopically outlined. We correlated histologic findings, macroscopic images and CECT.Results: Three ablation zones were identified in histological and macroscopic findings. Only one ablation zone could be depicted in CECT. Close conformity was observed between histological and macroscopic findings. The ablation zone depicted in CECT overestimated the histological avital central zone and inner red zone (p < = .01). No differences were found between CECT and the histological outer red zone (p > .05).Conclusions: Immediate post-interventional CECT overestimated the clinically relevant zone of complete cell ablation after MWA in porcine liver in vivo. This entails the risk of incomplete tumor ablation and could lead to tumor recurrence.

Is lung density associated with severity of COVID-19?

PURPOSE: Emphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19. MATERIAL AND METHODS: Thirty COVID-19-positive patients were included in this retrospective study. Lung density was quantified based on routinely acquired chest CTs. Presence of COVID-19 was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Wilcoxon test was used to compare two groups of patients. A multivariate regression analysis, adjusted for age and sex, was employed to model the relative increase of risk for severe disease, depending on the measured densities. RESULTS: Intensive care unit (ICU) patients or patients requiring mechanical ventilation showed a lower proportion of medium- and low-density lung volume compared to patients on the normal ward, but a significantly larger volume of high-density lung volume (12.26 dl IQR 4.65 dl vs. 7.51 dl vs. IQR 5.39 dl, p = 0.039). In multivariate regression analysis, high-density lung volume was identified as a significant predictor of severe disease. CONCLUSIONS: The amount of high-density lung tissue showed a significant association with severe COVID-19, with odds ratios of 1.42 (95% CI: 1.09-2.00) and 1.37 (95% CI: 1.03-2.11) for requiring intensive care and mechanical ventilation, respectively. Acknowledging our small sample size as an important limitation; our study might thus suggest that high-density lung tissue could serve as a possible predictor of severe COVID-19.

Comparison of different 4D CT-Perfusion algorithms to visualize lesions after microwave ablation in an in vivo porcine model.

Background: Accurate lesion visualization after microwave ablation (MWA) remains a challenge. Computed tomography perfusion (CTP) has been proposed to improve visualization, but it was shown that different perfusion-models delivered different results on the same data set.Purpose: Comparison of different perfusion algorithms and identification of the algorithm enables for the best imaging of lesion after hepatic MWA.Materials and methods: 10 MWA with consecutive CTP were performed in healthy pigs. Parameter-maps were generated using a single-input-dual-compartment-model with Patlak's algorithm (PM), a dual-input-maximum-slope-model (DIMS), a dual-input-one-compartment-model (DIOC), a single-(SIDC) and dual-input-deconvolution-model (DIDC). Parameter-maps for hepatic arterial (AF) and portal venous blood flow (PF), mean transit time, hepatic blood volume (HBV) and capillary permeability were compared regarding the values of the normal liver tissue (NLT), lesion, contrast- and signal-to-noise ratios (SNR, CNR) and inter- and intrarater-reliability using the intraclass correlation coefficient, Bland-Altman plots and linear regression.Results: Perfusion values differed between algorithms with especially large fluctuations for the DIOC. A reliable differentiation of lesion margin appears feasible with parameter-maps of PF and HBV for most algorithms, except for the DIOC due to large fluctuations in PF. All algorithms allowed for a demarcation of the central necrotic zone based on hepatic AF and HBV. The DIDC showed the highest CNR and the best inter- and intrarater reliability.Conclusion: The DIDC appears to be the most feasible model to visualize margins and necrosis zones after microwave ablation, but due to high computational demand, a single input deconvolution algorithm might be preferable in clinical practice.

Incidence of combined cranial and cervical spine injuries in patients with blunt minor trauma: are combined CT examinations of the head and cervical spine justified?

Background The use of computed tomography (CT) scans of the head and cervical spine has markedly increased in patients with blunt minor trauma. The actual likelihood of a combined injury of head and cervical spine following a minor trauma is estimated to be low. Purpose To determine the incidence of such combined injuries in patients with a blunt minor trauma in order to estimate the need to derive improved diagnostic guidelines. Material and Methods A total of 1854 patients were retrospectively analyzed. All cases presented to the emergency department and in all patients combined CT scans of head and cervical spine were conducted. For the following analysis, only 1342 cases with assured blunt minor trauma were included. Data acquisition covered age, sex, and presence of a head injury as well as presence of a cervical spine injury or both. Results Of the 1342 cases, 46.9% were men. The mean age was 65.6 years. CT scans detected a head injury in 116 patients; of these, 70 cases showed an intracranial hemorrhage, 11 cases a skull fracture, and 35 cases an intracranial hemorrhage as well as a skull fracture. An injury of the cervical spine could be detected in 40 patients. A combined injury of the head and cervical spine could be found in one patient. Conclusion The paradigm of the coincidence of cranial and cervical spine injuries should be revised in patients with blunt minor trauma. Valid imaging decision algorithms are strongly needed to clinically detect high-risk patients in order to save limited resources.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology.

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

BACKGROUND AND OBJECTIVES: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs. METHODS: To improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité - University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20). RESULTS: The final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm. CONCLUSIONS: Using an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.

Do submillisievert-chest CT protocols impact diagnostic quality in suspected COVID-19 patients?

BACKGROUND: During the ongoing global SARS-CoV-2 pandemic, there is a high demand for quick and reliable methods for early identification of infected patients. Due to its widespread availability, chest-CT is commonly used to detect early pulmonary manifestations and for follow-ups. PURPOSE: This study aims to analyze image quality and reproducibility of readings of scans using low-dose chest CT protocols in patients suspected of SARS-CoV-2 infection. MATERIALS AND METHODS: Two radiologists retrospectively analyzed 100 low-dose chest CT scans of patients suspected of SARS-CoV-2 infection using two protocols on devices from two vendors regarding image quality based on a Likert scale. After 3 weeks, quality ratings were repeated to allow for analysis of intra-reader in addition to the inter-reader agreement. Furthermore, radiation dose and presence as well as distribution of radiological features were noted. RESULTS: The exams' effective radiation doses were in median in the submillisievert range (median of 0.53 mSv, IQR: 0.35 mSv). While most scans were rated as being of optimal quality, 38% of scans were scored as suboptimal, yet only one scan was non-diagnostic. Inter-reader and intra-reader reliability showed almost perfect agreement with Cohen's kappa of 0.82 and 0.87. CONCLUSION: Overall, in this study, we present two protocols for submillisievert low-dose chest CT demonstrating appropriate or better image quality with almost perfect inter-reader and intra-reader agreement in patients suspected of SARS-CoV-2 infection.

Successful treatment of severe COVID-19 pneumonia, a case series with simultaneous interleukin-1 and interleukin-6 blockade with 1-month follow-up.

Interleukin (IL)-6 and IL-1 blockade showed beneficial results in patients with severe COVID-19 pneumonia and evidence of cytokine release at the early disease stage. Here, we report outcomes of open-label therapy with a combination of blocking IL-6 with tocilizumab 8 mg/kg up to 800 mg and IL-1 receptor antagonist anakinra 100-300 mg over 3-5 days. Thirty-one adult patients with severe COVID-19 pneumonia and signs of cytokine release, mean age 54 (30-79) years, 5 female, 26 male, and mean symptom duration 6 (3-10) days were treated. Patients with more than 10 days of symptoms, evidence of bacterial infection/elevated procalcitonin and other severe lung diseases were excluded. Computed tomography (CT) scans of the lung were performed initially and after 1 month; inflammatory activity was assessed on a scale 0-25. Twenty-five patients survived without intubation and mechanical lung ventilation, two patients died. C-reactive protein decreased in 19/31 patients to normal ranges. The mean activity CT score decreased from 14 (8-20) to 6 (0-16, n = 16). In conclusion, most of our patients recovered fast and sustained, indicating that early interruption of cytokine release might be very effective in preventing patients from mechanical ventilation, death, and long-term damage.

Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine.

OBJECTIVES: Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patients. Therefore, we aimed to develop and evaluate deep learning models for the identification of clinically relevant abnormalities in bedside CXRs, using reference standards established by computed tomography (CT) and multiple radiologists. MATERIALS AND METHODS: In this retrospective study, a dataset consisting of 18,361 bedside CXRs of patients treated at a level 1 medical center between January 2009 and March 2019 was used. All included CXRs occurred within 24 hours before or after a chest CT. A deep learning algorithm was developed to identify 8 findings on bedside CXRs (cardiac congestion, pleural effusion, air-space opacification, pneumothorax, central venous catheter, thoracic drain, gastric tube, and tracheal tube/cannula). For the training dataset, 17,275 combined labels were extracted from the CXR and CT reports by a deep learning natural language processing (NLP) tool. In case of a disagreement between CXR and CT, human-in-the-loop annotations were used. The test dataset consisted of 583 images, evaluated by 4 radiologists. Performance was assessed by area under the receiver operating characteristic curve analysis, sensitivity, specificity, and positive predictive value. RESULTS: Areas under the receiver operating characteristic curve for cardiac congestion, pleural effusion, air-space opacification, pneumothorax, central venous catheter, thoracic drain, gastric tube, and tracheal tube/cannula were 0.90 (95% confidence interval [CI], 0.87-0.93; 3 radiologists on the receiver operating characteristic [ROC] curve), 0.95 (95% CI, 0.93-0.96; 3 radiologists on the ROC curve), 0.85 (95% CI, 0.82-0.89; 1 radiologist on the ROC curve), 0.92 (95% CI, 0.89-0.95; 1 radiologist on the ROC curve), 0.99 (95% CI, 0.98-0.99), 0.99 (95% CI, 0.98-0.99), 0.98 (95% CI, 0.97-0.99), and 0.99 (95% CI, 0.98-1.00), respectively. CONCLUSIONS: A deep learning model used specifically for bedside CXRs showed similar performance to expert radiologists. It could therefore be used to detect clinically relevant findings during after-hours and help emergency and intensive care physicians to focus on patient care.

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition.

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse® and 14 with corpuls cpr®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.

Improving CT accuracy in the diagnosis of COVID-19 in a hospital setting.

OBJECTIVE: This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging. METHODS: A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O2-saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p-value of <0.05 was considered statistically significant. RESULTS: The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups. CONCLUSIONS: The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19.