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COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

OBTECTIVE: To determine the incremental yield of standardized addition of chest CT to abdominal CT to detect COVID-19 in patients presenting with primarily acute gastrointestinal symptoms requiring abdominal imaging. SUMMARY BACKGROUND DATA: Around 20% of patients with COVID-19 present with gastrointestinal symptoms. COVID-19 might be neglected in these patients, as the focus could be on finding abdominal pathology. During the COVID-19 pandemic several centers have routinely added chest CT to abdominal CT to detect possible COVID-19 in patients presenting with gastrointestinal symptoms. However, the incremental yield of this strategy is unknown. METHODS: This multicenter study in six Dutch centers included consecutive adult patients presenting with acute non-traumatic gastrointestinal symptoms, who underwent standardized combined abdominal and chest CT between March 15, 2020 and April 30, 2020. All CT scans were read for signs of COVID-19 related pulmonary sequelae using the CO-RADS score. The primary outcome was the yield of high COVID-19 suspicion (CO-RADS 4-5) based on chest CT. RESULTS: A total of 392 patients were included. Radiologic suspicion for COVID-19 (CO-RADS 4-5) was present in 17 (4.3%) patients, eleven of which were diagnosed with COVID-19. Only five patients with CO-RADS 4-5 presented without any respiratory symptoms and were diagnosed with COVID-19. No relation with community prevalence could be detected. CONCLUSION: The yield of adding chest CT to abdominal CT to detect COVID-19 in patients presenting with acute gastrointestinal symptoms is extremely low with an additional detection rate of around 1%.

To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease severity in patients with emphysema.Our IRB approved HIPAA-compliant study included 113 adults (71±8 years; 47 females, 66 males) who had both non-contrast chest CT and pulmonary function tests performed within a span of 2 months. The disease severity was classified based on the forced expiratory volume in 1 second (FEV1 as % of predicted) into mild, moderate, and severe. 2 thoracic radiologists (RA), blinded to the clinical and AI results, graded severity of emphysema on a 5-point scale suggested by the Fleischner Society for each lobe. The whole lung scores were derived from the summation of lobar scores. Thin-section CT images were processed with the AI-Rad Companion Chest prototype (Siemens Healthineers) to quantify low attenuation areas (LAA- 950 HU) in whole lung and each lobe separately. Bronchial abnormality was assessed by both radiologists and a fully automated software (Philips Healthcare).Both AI (AUC of 0.77; 95% CI: 0.68 - 0.85) and RA (AUC: 0.76, 95% CI: 0.65 - 0.84) emphysema quantification could differentiate mild, moderate, and severe disease based on FEV1. There was a strong positive correlation between AI and RA (r = 0.72 - 0.80; p0.001). The combination of emphysema and bronchial abnormality quantification from radiologists' and AI assessment could differentiate between different severities with AUC of 0.80 - 0.82 and 0.87, respectively.The assessed AI-prototypes can predict the disease severity in patients with emphysema with the same predictive value as the radiologists.

Background The chest CT manifestations of COVID-19 from hospitalization to convalescence after one year are not known. Purpose To assess chest CT manifestations of COVID-19 up to 1 year after symptom onset. Materials and Methods Patients were enrolled if they were admitted to the hospital due to COVID-19 and underwent CT scans during hospitalization at two isolation centers between 27 January and 31 March 2020. In a prospective study, three serial chest CTs were obtained at approximately 3, 7, and 12 months after symptom onset and longitudinally analyzed. The total CT score of pulmonary lobe involvement from 0 to 25 was assessed (score 1-5 for each lobe). Uni-/multi-variable logistic regression tests were performed to explore independent risk factors for residual CT abnormalities after one year. Results 209 study participants (mean age: 49±13 years, 116 women) were evaluated. At 3 months, 61% of participants (128 of 209) had resolution of CT abnormalities; at 12 months, 75% (156 of 209) had resolution. Of chest CT abnormalities that had not resolved, there were residual linear opacities in 25/209 (12%) and multifocal reticular/cystic lesions in 28/209 (13%) participants. Age≥50 years, lymphopenia, and severe/ARDS aggravation were independent risk factors for residual CT abnormalities at one year (odds ratios of 15.9, 18.9, and 43.9, respectively; P

Objective The purpose of this study was to develop a natural language processing (NLP) pipeline to identify incidental thyroid nodules (ITNs) meeting criteria for sonographic follow-up and to assess both adherence rates to white paper recommendations and downstream outcomes related to these incidental findings. Methods 21583 non-contrast chest CT reports from 2017 and 2018 were retrospectively evaluated to identify reports which included either an explicit recommendation for thyroid ultrasound, a description of a nodule ≥ 1.5 cm, or description of a nodule with suspicious features. Reports from 2018 were used to train an NLP algorithm called fastText for automated identification of such reports. Algorithm performance was then evaluated on the 2017 reports. Next, any patient from 2017 with a report meeting criteria for ultrasound follow-up was further evaluated with manual chart review to determine follow-up adherence rates and nodule-related outcomes. Results NLP identified reports with ITNs meeting criteria for sonographic follow-up with an accuracy of 96.5% (95% CI 96.2-96.7) and sensitivity of 92.1% (95% CI 89.8-94.3). In 10006 chest CTs from 2017, ITN follow-up ultrasound was indicated according to white paper criteria in 81 patients (0.8%), explicitly recommended in 46.9% (38/81) of patients, and obtained in less than half of patients in which it was appropriately recommended (17/35, 48.6%). Discussion NLP accurately identified chest CT reports meeting criteria for ITN ultrasound follow-up. Radiologist adherence to white paper guidelines and subsequent referrer adherence to radiologist recommendations showed room for improvement.

Graphical abstract, Introduction Impairment in pulmonary function tests and radiological abnormalities are a major concern in COVID-19 survivors. Our aim is to evaluate functional respiratory parameters, changes in chest CT, and correlation with peripheral blood biomarkers involved in lung fibrosis at two and six months after SARS-CoV-2 pneumonia. Methods COVID-FIBROTIC (clinicaltrials.gov NCT04409275) is a multicenter prospective observational cohort study aimed to evaluate discharged patients. Pulmonary function tests, circulating serum biomarkers, chest radiography and chest CT were performed at outpatient visits. Results In total, 313, aged 61.12 ± 12.26 years, out of 481 included patients were available. The proportion of patients with DLCO

Rationale and Objectives A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study was to evaluate the accuracy of the developed modified 3D U-net deep-learning model. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant, Institutional Review Board-approved retrospective study, the 3D U-net based deep-learning model was trained using the Lung Image Database Consortium and Image Database Resource Initiative dataset. For internal model validation, we used 89 chest CT scans that were not used for model training. For external model validation, we used 450 chest CT scans taken at an urban university hospital in Japan. Each case included at least one nodule of >5 mm identified by an experienced radiologist. We evaluated model accuracy using the competition performance metric (CPM) (average sensitivity at 1/8, 1/4, 1/2, 1, 2, 4, and 8 false-positives per scan). The 95% confidence interval (CI) was computed by bootstrapping 1000 times. Results In the internal validation, the CPM was 94.7% (95% CI: 89.1%–98.6%). In the external validation, the CPM was 83.3% (95% CI: 79.4%–86.1%). Conclusion The modified 3D U-net deep-learning model showed high performance in both internal and external validation.

Recent literature has raised concerns about the sensitivity and accuracy of radiographs at diagnosing rib fractures. Studies have shown that chest computed tomography (CT) has far greater sensitivity at detecting rib fractures than radiographs. The purpose of this study was to evaluate the sensitivity of skeletal survey (SS) radiographs at diagnosis of rib fractures compared to CT in the pediatric population. This retrospective review included 57 patients who had undergone both a SS and a CT chest or CT chest/abdomen/pelvis within 30 days of each other for the indication of either non-accidental (NAT) or accidental trauma between 2009 and 2017. Images and reports were analyzed by a pediatric radiology fellow for presence/absence of fracture, evidence of healing and location of rib fracture, including rib level, location within the rib (anterior, lateral, and posterior), and side (right versus left). 225 rib fractures were identified in 25 patients on CT. 38 of those fractures were missed on the preceding SS, yielding a miss rate of 17%. Acute fractures were more likely to be missed than chronic or healing fractures (p ≤ 0.01). Location within the rib did not impact rib detection on radiographs. Left-sided rib fractures were not more common in NAT patients, compared to accidental trauma. SS miss approximately 17% of all rib fractures and CT is more sensitive modality in the detection of rib fractures, particularly acute rib fractures, regardless of location. Low-dose Chest CT could be a helpful modality in the work-up of NAT trauma.

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew's correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases. © 2021 Tech Science Press. All rights reserved.

Background Incidental breast cancers can be detected on chest computed tomography (CT) scans. With the use of deep learning, the sensitivity of incidental breast cancer detection on chest CT would increase. This study aimed to evaluate the performance of deep learning algorithm to detect breast cancers on chest CT and to validate the results in the internal and external data sets. Materials and Methods This retrospective study collected 1,170 preoperative chest CT scans after the diagnosis of breast cancer for algorithm development (n=1,070), internal test (n=100), and external test (n=100). Deep learning algorithm based on the RetinaNet was developed and tested to detect breast cancer on chest CT. Results In the internal test set, the algorithm detected 96.5% of breast cancers with 13.5 false-positives per case (FPs/case). In the external test set, the algorithm detected 96.1% of breast cancers with 15.6 FPs/case. When the candidate probability of 0.3 was used as the cutoff value, the sensitivities were 92.0% with 7.36 FPs/case for the internal test set and 93.0% with 8.85 FPs/case for the external test set. When the candidate probability of 0.4 was used as the cutoff value, the sensitivities were 88.5% with 5.24 FPs/case in the internal test set and 90.7% with 6.3 FPs/case in the external test set. Conclusion Deep learning algorithm could sensitively detect breast cancer on chest CT in both the internal and external test sets. Microabstract The sensitivity of incidental breast cancer detection on chest CT would increase with the use of deep learning. We evaluated performance of deep learning algorithm to detect breast cancers on chest CT and validated the results. Deep learning algorithm could sensitively detect breast cancer on chest CT in both the internal and external test sets.

Evolution of the multimodality management of early lung cancer, including progress in surgical techniques, has introduced the possibility of resection for lung cancer cases that historically were considered unresectable (e.g., select cases of T4 disease and oligometastatic disease). However, the TNM classification does not uniformly correlate with lung cancer operability and resectability. Radiologic evaluation is therefore critical in identifying patients' suitability to undergo lung cancer resection and in guiding the selection of a surgical approach from among a range of such approaches, including wedge resection, segmentectomy, lobectomy, bilobectomy, and pneumonectomy. The radiologist must understand the available surgical options, along with their advantages and disadvantages, and provide a report that includes critical information on tumor size, location, and extension and anatomic relations that may influence the surgical technique. Preoperative CT findings may also help predict expected postoperative lung function and the associated impact on the postoperative course of the patient. This article reviews the role of chest CT in the preoperative evaluation of lung cancer, focusing on the key CT findings that help direct surgical decision making in the context of an expanding range of patients who may be considered candidates for resection.

Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/ isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis. COVID-view incorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist using COVID-view and provides the attention heatmap as an explainable DL for the model output. We designed and evaluated COVID-view through suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases., Comment: 11 pages, 10 figures, accepted to IEEE VIS 2021 conference and IEEE Transactions on Visualization and Computer Graphics

A 63-year-old asymptomatic woman with abnormal chest shadow was referred to our hospital. The patient was a never smoker, and had a history of bronchial asthma and hypertension. Chest CT revealed a 4 cm diameter tumour in the right upper lobe, two nodules accompanied spicula and pleural indentation

Purpose: The purpose of this study was to investigate the influencing factors for chest CT hysteresis and severity of coronavirus disease 2019 (COVID-19). Methods: The chest CT data of patients with confirmed COVID-19 in 4 hospitals were retrospectively analyzed. An independent assessment was performed by one clinician using the DEXIN FACT Workstation Analysis System, and the assessment results were reviewed by another clinician. Furthermore, the mean hysteresis time was calculated according to the median time from progression to the most serious situation to improve chest CT in patients after fever relief. The optimal scaling regression analysis was performed by including variables with statistical significance in univariate analysis. In addition, a multivariate regression model was established to investigate the relationship of the percentage of lesion/total lung volume with lymphocyte and other variables. Results: In the included 166 patients with COVID-19, the average value of the most serious percentage of lesion/total lung volume was 6.62, of which 90 patients with fever had an average hysteresis time of 4.5 days after symptom relief, with a similar trend observed in those without fever. Multivariate analysis revealed that lymphocyte count in peripheral blood and transcutaneous oxygen saturation decreased with the increase of the percentage of lesion/total lung volume. Conclusion: There is a hysteresis effect in the improvement of chest CT image relative to fever relief in patients with COVID-19. The pulmonary lesions may be related to the severity as well as decreased lymphocyte count or percutaneous oxygen saturation.

Purpose This retrospective analysis aims to examine the effectiveness of the current chest imaging guidelines regarding COVID-19 positive pediatric patients on our study group of patients aged 0 to 18. Materials and methods We examined clinical and imaging data of 178 pediatric COVID-19 positive patients confirmed by PCR admitted to the Children's Hospital of Los Angeles between March 6, 2020 and June 23, 2020. Results Of 178 patients, only 46 (27%) patients underwent any form of chest imaging. Thirteen (28%) of 46 imaged patients had positive chest X-rays (CXR) or computed tomography (CT) chest findings, with 8 (62%) of the 13 patients suggesting pneumonia or multifocal pneumonia, 3 (23%) patients having acute respiratory distress syndrome, and 2 (15%) patients demonstrating left sided pleural effusions thought to be the result of ruptured appendicitis unrelated to their COVID-19 diagnosis. All but one patient had significant prior medical histories with an associated comorbid medical condition. Of the 46 imaged patients, 17 (37%) patients had a negative chest X-ray, and 15 (33%) patients had suggestive findings of viral etiology. 132 patients were not imaged. Conclusion Our study population corroborated current chest imaging guidelines in pediatric patients. Chest imaging modalities such as CXR and CT should be reserved for patients who are severely symptomatic and/or possess prior comorbidities such as immunosuppression, diabetes, asthma, obesity, or where other differential etiologies must be entertained.

RT-PCR is the gold standard in the diagnosis of COVID-19 infections, due to its high specificity. However, there are clinical situations in which chest CT may prove vital, for example in patients with high clinical and epidemiologic suspicion towards COVID-19 before positive RT-PCR conversion or in detecting complications. Researchers have developed scales that, based on the findings in chest CT, help predict the severity of the disease. There are three main pathologic patterns of lung injury that correlate with the duration of COVID-19 symptoms. Epithelial pattern with diffuse alveolar damage and desquamation/reactive hyperplasia of pneumocytes; vascular pattern with capillary congestion and (micro)thrombi and fibrotic pattern with interstitial fibrous changes. The epithelial pattern and vascular pattern appear early, even before the symptoms of the disease, whereas the fibrous pattern appears approximately three weeks after the onset of the disease. Typical findings on chest CT in COVID-19 infection are: GGO, consolidation, GGO mixed with consolidation, interlobular septal thickening, air bronchogram sign, crazy paving, bronchial wall thickening and vascular enlargement. Findings that may suggest a different etiology include multiple nodules, tree-in-bud opacities, bronchiectasis, pleural and pericardial effusion, extensive consolidations.

The purpose of this study was to examine the prevalence and characteristics of thoracic diffuse idiopathic skeletal hyperostosis (T-DISH) in the Black patients using the computed tomography (CT) analysis. This study is a cross-sectional study. All patients who underwent chest CT for the trauma screening and whose race was categorized as “Black” on the questionnaire were recruited in the study from Mar 2019 to Mar 2020. Demographic data, including age, sex, body mass index (BMI), and presence of diabetes mellitus (DM), were recorded. A total of 3299 Black patients (1507 women and 1792 men) were included for the analysis. The prevalence of T-DISH was 7.7% (255 patients), with 8.6% for females and 7.0% for males. The highest prevalence was observed in patients at the age of 70 years (11.7%), followed by the age of 80 years (10.5%). The highest prevalence level of T-DISH segment was at T8, followed by T9, and T7. The most frequent number of contiguous vertebrae was seven (21%). BMI was not associated with T-DISH. The presence of DM was significantly higher in male patients with T-DISH than those without T-DISH (P = 0.02).

Background Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients. Results A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose. Conclusion The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.

Purpose We aimed to compare COVID-19 imaging findings of young adults (19–35 years of age) with those of children (0–18 years) and to correlate imaging findings of young adults with their laboratory tests. Materials and methods This retrospective study included Real Time-Polymerase Chain Reaction (RT-PCR) confirmed 130 young adults (mean age: 28.39 ± 4.77; 65 male, 65 female) and 36 children (mean age: 12.41 ± 4.51; 17 male, 19 female), between March and June 2020. COVID-19 related imaging findings on chest CT were examined in young adults and compared with children by the Mann-Whitney U, and Chi-square or Fisher's exact test. Laboratory examinations of young adults were assessed in terms of correlation with radiological findings by the Spearman's correlation analysis. Results Bilateral multiple distributions (p = 0.014), subpleural involvement, and pleural thickening (p = 0.004), GGOs with internal consolidations were more frequent in adults (p = 0.009). Infiltrations were significantly larger than 20 mm in young adults (p = 0.011). The rates of feeding vessel sign, vascular enlargement, and halo sign were significantly higher in young adults (p

Coronavirus disease 2019 (COVID-19) pneumonia computed tomography imaging features have been described in detail in many studies. The pseudocavitation sign has not been described in the previous COVID-19 studies. We present chest computed tomography scans of 5 reverse transcriptase-polymerase chain reaction positive patients with COVID-19 pneumonia who has bare areas among pulmonary infiltrates. All 5 also had previous scans with similarly sized low attenuated areas in the same location prior to the addition of pulmonary infiltrates. The pre-existing cystic changes had become remarkable due to the contrast around them after the pulmonary infiltrates added. Therefore, they should be termed as “pseodocavity” according to Fleischner Society glossary. Small air-containing spaces between pulmonary infiltrates have been termed in previous COVID-19 studies as a new sign called “round cystic changes/air bubble sign/vacuolar sign.” We would like to draw attention that the vacuolar sign and the synonyms may be the pseudocavity sign that is due to pre-existing changes rather than a new defined sign.

OBJECTIVE: The purpose was to compare the results of the RT-PCR test, with the findings of Chest CT and to determine the features of CT for the diagnosis of COVID-19 and how to approach RT-PCR negative patients. MATERIAL METHOD: Chest CT findings of 569 COVID-19 diagnosed patients, followed up at the pandemic wards between March and June 2020 were retrospectively examined. Patients were grouped according to RT-PCR results, gender, and age. RESULTS: 284 (49%) were RT-PCR(+), 285 (50.8%) were RT-PCR(-) of total 569 patients. 11 (1.9%) of RT-PCR(+) had no involvement in Chest CT while all the RT-PCR(-) patients were CT(+). The distribution of lesions in CT were; 544 (95.6%) bilateral, 553 (97.2%) multilobar, 557(98%) peripherally 151 (26.5%) posteriorly localized. The most common findings were; 539 (94.7%) ground-glass opacity (GGO), 365 (64.1%) consolidation, 160 (28.1%) crazy paving interlobular septal thickening. CO-RADS mean value was 5.4 ± 0.7. GGO and reticulation in RT-PCR(-) patients were 280 (98.2%) and 24 (8.4%); while they were 259 (91.2%) and 12 (4.2%) in RT-PCR(+) patients, were significantly higher (p

BackgroundTo classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images.ResultsA convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models.ConclusionsThe COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.

BACKGROUND: This study aimed to explore the imaging characteristics, diversity and changing trend in CT scans of pediatric patients infected with Delta-variant strain by studying imaging features of children infected with Delta and comparing the results to those of children with original COVID-19. METHODS: A retrospective, comparative analysis of initial chest CT manifestations between 63 pediatric patients infected with Delta variant in 2021 and 23 pediatric patients with COVID-19 in 2020 was conducted. Corresponding imaging features were analyzed. In addition, the changing trend in imaging features of COVID-19 Delta-variant cases were explored by evaluating the initial and follow-up CT scans. RESULTS: Among 63 children with Delta-variant COVID-19 in 2021, 34 (53.9%) showed positive chest CT presentation; and their CT score (1.10 ± 1.41) was significantly lower than that in 2020 (2.56 ± 3.5) (P = 0.0073). Lesion distribution: lung lesions of Delta cases appear mainly in the lower lungs on both sides. Most children had single lobe involvement (18 cases, 52.9%), 14 (41.2%) in the right lung alone, and 14 (41.2%) in both lungs. A majority of Delta cases displayed initially ground glass (23 cases, 67.6%) and nodular shadows (13 cases, 38.2%) in the first CT scan, with few extrapulmonary manifestations. The 34 children with abnormal chest CT for the first time have a total of 92 chest CT examinations. These children showed a statistically significant difference between the 0-3 day group and the 4-7 day group (P = 0.0392) and a significant difference between the 4-7 day group and the more than 8 days group (P = 0.0003). CONCLUSIONS: The early manifestations of COVID-19 in children with abnormal imaging are mostly small subpleural nodular ground glass opacity. The changes on the Delta-variant COVID-19 chest CT were milder than the original strain. The lesions reached a peak on CT in 4-7 days and quickly improved and absorbed after a week. Dynamic CT re-examination can achieve a good prognosis.

Introduction: We aimed to evaluate clinical and laboratory findings of hospitalized asthma and chronic obstructive pulmonary disease (COPD) patients with COVID-19 and demonstrate that they have different symptoms and/or laboratory results and outcomes than COVID-19 patients with comorbidity (CoV-com) and without comorbidity (CoV-alone). Methodology: The data of the demographic, clinical, laboratory findings of hospitalized CoV-alone, asthma, COPD patients with COVID-19 (CoV-asthma, CoV-COPD, respectively), and CoV-com were analyzed. Results: Out of 1082 patients hospitalized for COVID-19, 585 (54.1%) had CoV-alone, 40 (3.7%) had CoV-asthma, 46 (4.3%) had CoV-COPD and 411 (38%) had CoV-com. Cough, shortness of breath, fever and weakness were the most common four symptoms seen in all COVID-19 patients. Shortness of breath, myalgia, headache symptoms were more common in CoV-asthma than the other groups (p < 0.001, p < 0.01, p < 0.05 respectively). Sputum was more common in CoV-COPD than other groups (p < 0.01). COPD group most frequently had increased values, different from the other groups with CRP>5ng/mL in 91.3%, D-dimer > 0.05mg/dL in 89.1%, troponin > 0.014micg/L in %63.9, INR>1.15 in 52.2%, CK-MB>25U/L in 48.5%, PT>14s in 40.9% of patients (p < 0.05, p < 0.001, p < 0.001, p < 0.001, p < 0.05, p < 0.001, respectively). NT-ProBNP was found to have the highest AUC value and the best differentiating parameter for CoV-asthma from CoV-alone. Typical CT findings were present in 44.4% of CoV-alone, 57.5% of CoV-asthma, 28.3% of CoV-COPD and 38.9% of CoV-com groups. CoV-COPD and CoV-com patients died more frequently than other groups (17.8%, 18.5%). Conclusions: CoV-asthma and CoV-COPD patients might have different symptoms and laboratory parameters than other COVID-19 patients which can guide the physicians.

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

Objective: The aim of this study was to explore the chest Computer Tomography (CT) features of COVID-19 patients and compare the diagnostic performance of chest CT with that of RT-PCR at our center. Material and Methods: The study was conducted by retrospectively scanning the files of patients admitted to the Emergency Department of Karaman State Hospital under suspicion of COVID-19 between March 15 and May 18, 2020. The study included 218 patients (124 males, 94 females, 1-90 years of age, median 48.5) who met the inclusion criteria. Chest CT scan findings were recorded in detail by two radiologists with experience in chest radiology. Furthermore, the CT findings were categorized into four groups in the form of typical, indeterminate, atypical, and negative CT according to the structured reporting system. Results: The RT-PCR results were positive in 41% of 139 cases with positive CT results, while the RT-PCR results were positive in 15.2% of the cases with negative CT results. Regarding the analysis between CT and RT-PCR, the sensitivity was 82.61%, the specificity was 44.97%, the PPV was 41.01%, the NPV was 84.81%. The positivity rate was 82.9% in those with typical imaging findings, 51.5% in those assessed as indeterminate. Those with a typical appearance had a higher rate of positivity than others. Conclusion: The chest CT scan was found to have a high correlation with the RT-PCR in the patients with typical imaging findings in CT. The specificity was found to be low in the CT scan findings without subgroup analysis.

To estimate the effectiveness and efficiency of chest CT in children based on the suspected diagnosis in relation to the number of positive, negative, and inconclusive CT results.In this monocentric retrospective study at a university hospital with a division of pediatric radiology, 2019 chest CT examinations (973 patients; median age: 10.5 years; range: 2 days to 17.9 years) were analyzed with regards to clinical data, including the referring department, primary questions or suspected diagnosis, and CT findings. It was identified if the clinical question was answered, whether the suspected diagnosis was confirmed or ruled out, and if additional findings (clinically significant or minor) were detected.The largest clinical subgroup was the hematooncological subgroup (n = 987), with frequent questions for inflammation/pneumonia (66 % in this subgroup). Overall, CT provided conclusive results in 97.6 % of all scans. In 1380 scans (70 %), the suspected diagnosis was confirmed. In 406/2019 cases (20 %), the CT scan was negative also in terms of an additional finding. In 8 of 9 clinical categories, the proportion of positive results was over 50 %. There were predominantly negative results (110/179; 61 %) in pre-stem cell transplant evaluation. In the subgroup of trauma management, 81/144 exams (57 %) showed positive results, including combined injuries (n = 23). 222/396 (56 %) of all additional findings were estimated to be clinically significant.In a specialized center, the effectiveness of pediatric chest CT was excellent when counting the conclusive results. However, to improve efficiency, the clinical evaluation before imaging appears crucial to prevent unnecessary CT examinations.· Pediatric chest CT in specialized centers has a high diagnostic value.. · CT identifies relevant changes besides the working hypothesis in clinically complex situations.. · Pre-CT clinical evaluation is crucial, especially in the context of suspected pneumonia..· Esser M, Tsiflikas I, Kraus MS et al. Effectiveness of Chest CT in Children: CT Findings in Relation to the Clinical Question. Fortschr Röntgenstr 2022; 194: 281 - 290.ZIEL: Abschätzung der klinischen Wertigkeit der Thorax-CT bei Kindern auf Grundlage der Verdachtsdiagnose und positiven, negativen und inkonklusiven CT-Befunde.In dieser retrospektiven monozentrischen Studie an einer Universitätsklinik mit Abteilung für Kinderradiologie wurden 2019 Thorax-CTs (973 Patienten; Altersmedian 10,5 Jahre; Spannweite: 2 Tage bis 17,9 Jahre) hinsichtlich klinischer Daten analysiert, einschließlich Zuweiserinformationen, primärer Fragestellungen oder Verdachtsdiagnosen und CT-Befunde. Es wurde festgehalten, ob die klinische Fragestellung beantwortet wurde, ob die Verdachtsdiagnose bestätigt oder ausgeschlossen wurde und ob Nebenbefunde (klinisch signifikant oder irrelevant) festgestellt wurden.Die größte klinische Untergruppe bestand aus hämatoonkologischen Fällen (n = 987) mit häufiger Fragestellung eines pulmonalen Infektfokus (66 % in dieser Gruppe). Insgesamt lieferte die CT in 97,6 % aller CTs konklusive Resultate. In 1380 CTs (70 %) wurde die Verdachtsdiagnose bestätigt. In 406/2019 Fällen (20 %) wurde weder ein primärer Befund noch ein Nebenbefund festgestellt. In 8 von 9 klinischen Kategorien war der Anteil der positiven Ergebnisse über 50 %. Vorwiegend negative Ergebnisse (110/179; 61 %) traten bei Patienten vor Stammzelltransplantation auf. In der Gruppe der Traumapatienten lieferten 81/144 der Untersuchungen (57 %) positive Ergebnisse, einschließlich mehrerer kombinierter Verletzungen (n = 23). 222/396 aller Nebenbefunde (56 %) wurden als klinisch relevant eingestuft.Die pädiatrische Thorax-CT in spezialisierten Zentren hat eine hohe diagnostische Aussagekraft im Hinblick auf konklusive Untersuchungsergebnisse. Zur Verbesserung der Effizienz der Methode und Prävention unnötiger CT-Untersuchungen ist die klinische Beurteilung prä-CT entscheidend.· Die pädiatrische Thorax-CT in spezialisierten Zentren hat eine hohe diagnostische Aussagekraft.. · Die CT kann in klinisch komplexen Situationen relevante Veränderungen neben der Arbeitshypothese erkennen.. · Die klinische Beurteilung prä-CT ist unentbehrlich, insbesondere bei Verdacht auf eine Pneumonie..

Introduction Coronavirus disease 2019 (COVID‐19) has infected over 215 million individuals worldwide. Chest radiographs (CXR) and computed tomography (CT) have assisted with diagnosis and assessment of COVID‐19. Previous reports have described peripheral and lower zone predominant opacities on chest radiographs. Whilst the most common patterns on CT are bilateral, peripheral basal predominant ground glass opacities (Wong et al., Radiology, 296, 2020, E72; Karimian and Azami, Pol J Radiol, 86, 2021, e31). This study describes the imaging findings in an Australian tertiary hospital population. Methods COVID‐PCR‐positive patients who had chest imaging (CXR, CT and ventilation perfusion (V/Q) scans) from January 2020 to August 2020 were included. Distribution, location and pattern of involvement was recorded. Evaluation of the assessors was performed using Fleiss Kappa calculations for review of radiographic findings and qualitative analysis of CT findings. Results A total of 681 studies (616 CXRs, 59 CTs, 6 V/Q) from 181 patients were reviewed. The most common chest radiograph finding was bilateral lower lobe predominant diffuse opacification and most common CT pattern being ground glass opacities. Of the CT imaging, 33 were CT Pulmonary Angiograms of which five demonstrated acute pulmonary emboli. There was good inter‐rater agreement between radiologists in assessment of imaging appearances on CXR (kappa 0.29–0.73) and CT studies. Conclusion A review of imaging in an Australian tertiary hospital demonstrates similar patterns of COVID‐19 infection on chest X‐ray and CT imaging when compared to the international population.

One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID 19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based on ResNet 50, named CORNet for the detection of COVID-19, and also performed the retrospective and multicenter analysis for the extraction of visual characteristics from volumetric chest CT scans during COVID-19 detection. Between August 2016 and May 2020, the datasets were obtained from six hospitals. Results are evaluated on the image dataset consisting of a total of 10,052 CT scan images generated from 7850 patients, and the average age of the patients was 50 years. The implemented model has achieved the sensitivity and specificity of 90% and 96%, per scanned image with an AUC of 0. 95.

Background We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors. Methods Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers’ assessments were calculated. Results In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader’s sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14–1.30), 1.00 (1.00–1.01), 1.03 (1.02–1.04), 1.07 (1.03–1.11), and 1.02 (1.01–1.03) by using the CAD, respectively. Conclusion The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.

Summary Objective This study evaluated the differences in the appearance of COVID-19 pneumonia on chest computed tomography (CT) images of outpatient and cases that developed during hospitalisation. Method Chest CT images of 66 patients (median age, 76 years; range, 29–94 years) who underwent the severe acute respiratory syndrome coronavirus-2 reverse-transcription polymerase chain reaction (RT-PCR) test were included in this retrospective study. The chest CT appearance was categorised as “typical,” “indeterminate,” “atypical,” or “negative” in accordance with the recommendations of the Radiological Society of North America for COVID-19 pneumonia and compared among the following four subgroups: PCR-positive outpatient (n = 14); PCR-positive hospitalised (n = 7); PCR-negative outpatient (n = 9); and PCR-negative hospitalised (n = 36). Findings The frequency of “typical” findings in the PCR-positive outpatient cases (13/14, 92.9%) was significantly higher than that of those in the PCR-positive hospitalised cases (2/7, 28.6%, P = 0.022). There was no significant difference between the frequency of the “typical” appearance in PCR-positive hospitalised cases and that of those in the PCR-negative hospitalised cases (1/36, 2.8%, P = 0.192). Conclusions When COVID-19 patients acquire infections while hospitalised, their chest CT images are less likely to show typical findings than those of outpatient cases. Comprehensive and careful assessments of CT findings and consideration of the possibility of concomitant infections with other pathogens and clinical information, such as underlying diseases, background lung structure, and time course of the infection, are required for the management of such cases.

OBJECTIVE To comprehensively and accurately analyze the out-performance of low-dose chest CT (LDCT) vs. standard-dose CT (SDCT). METHODS The image quality, size measurements and radiation exposure for LDCT and SDCT protocols were evaluated. A total of 117 patients with extra-thoracic malignancies were prospectively enrolled for non-enhanced CT scanning using LDCT and SDCT protocols. Three experienced radiologists evaluated subjective image quality independently using a 5-point score system. Nodule detection efficiency was compared between LDCT and SDCT based on nodule characteristics (size and volume). Radiation metrics and organ doses were analyzed using Radimetrics. RESULTS The images acquired with the LDCT protocol yielded comparable quality to those acquired with the SDCT protocol. The sensitivity of LDCT for the detection of pulmonary nodules (n=650) was lower than that of SDCT (n=660). There was no significant difference in the diameter and volume of pulmonary nodules between LDCT and SDCT (for BMI 0.05). The individualized volume CT dose index (CTDIvol), the size specific dose estimate and effective dose were significantly reduced in the LDCT group compared with the SDCT group (all P

Background COVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day. Method This study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap. Results The proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%. Conclusion Our MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.

PURPOSE: We aimed to evaluate the use of the COVID-19 reporting and data system (CO-RADS) among radiologists and the diagnostic performance of this system. METHODS: Four radiologists retrospectively evaluated the chest CT examinations of 178 patients. The study included 143 patients with positive reverse transcriptase-polymerase chain reaction (RT-PCR) test results and 35 patients whose RT-PCR tests were negative but whose clinical and/or radiological findings were consistent with COVID-19. Fleiss’ kappa (κ) values were calculated, and individual observers’ scores were compared. To investigate diagnostic efficiency, receiver operating characteristic (ROC) curves were calculated for each interpreter. RESULTS: The interpreters were in full agreement on 574 of 712 (80.6%) evaluations. The common Fleiss’ κ value of all the radiologists combined was 0.712 (95% confidence interval [CI] 0.692–0.769). A reliable prediction on the basis of RT-PCR and clinical findings indicated the mean area under the curve (AUC) of Fleiss’ κ value as 0.89 (95% CI 0.708–0.990). General interpreter agreement was found to range from moderate to good. CONCLUSION: The interpreter agreement for CO-RADS categories 1 and 5 was reasonably good. We conclude that this scoring system will make a valuable contribution to efforts in COVID-19 diagnosis. CO-RADS can also be of significant value for the diagnosis and treatment of the disease in cases with false-negative PCR results.

Introduction Chest radiology provides an opportunity to better understand the diagnostic characteristics of e-cigarette or vaping product use associated lung injury (EVALI). This systematic review aimed to summarize the radiological findings associated with EVALI reported in the literature. Methods This systematic review was conducted in accordance with PRISMA guidelines. Electronic searches of MEDLINE®, CINAHL, Embase® and CENTRAL were conducted in February 2020. Included were all English-language studies reporting radiological findings of EVALI. Data was synthesized using descriptive statistics. Results Thirty studies comprising 184 participants were included. Mean patient age was 24.5 years old. The majority (n = 141, 76.6%) of included patients were male. The most common radiological features reported on chest x-ray were bilateral infiltrates (n = 64, 41.3%) and ground glass opacities (n = 17, 11.0%), and on chest CT were bilateral infiltrates (n = 62, 36.9%), bilateral ground glass opacities (n = 56, 33.3%), subpleural sparing (n = 29, 17.3%), pleural effusions (n = 14, 8.3%), and centrilobular nodularity (n = 13, 7.7%). Of patients with follow-up data reported (n = 81), only 28.4% (n = 23) had complete resolution of symptoms or radiological findings. Conclusion Chest radiology is the cornerstone of diagnosis and monitoring of EVALI. A wide variety of radiological findings highlight the need for standardisation of terminology in the radiological descriptions of EVALI. Common findings included bilateral infiltrates and ground glass opacities. Higher quality evidence is warranted to help develop evidenced-based guidelines for the diagnosis and management of EVALI.

BACKGROUND Chest CT scans are routinely obtained to monitor disease progression in pulmonary fibrosis. However, radiologists do not employ a standardized system for quantitative description of the severity of the disease. Development and validation of a grading system offers potential for enhancing the information that radiologists provide clinicians. STUDY DESIGN AND METHODS Our retrospective review analyzed 100 sequential patients with usual interstitial pneumonitis (UIP) on HRCT scans from 2018 and 2019. A radiologic scoring system evaluated the percent of normal lung on the basis of a 0-5 point scale per lobe (findings for the right middle lobe were included in the right upper lobe score), yielding an overall additive numerical score on a scale of 20 (completely normal lung) to 0 (no normal lung). Two radiologists quantified the percentage of normal lung by consensus agreement. Percent DLCO as well as demographic data were obtained from the medical record. Statistical analysis was performed using Spearman correlation to assess correlation between grade and percent DLCO. RESULTS 96 patients met the inclusion criteria; average age was 71, 68% were male. Score on CT scan ranged from 18 to 4; average 10.9, SD 3.58. The single-breath diffusing capacity (percent DLCO) ranged from 88% to 17%; mean 44.5%, SD 14.3%. Spearman's correlation for CT score and percent DLCO was 0.622, P

Background: Chest ultrasound (US) and color Doppler sonography play important roles in the diagnosis and charac-terization of different pathologies of peripheral lung lesions, especially in the emergency setting. Aim of Study: The aim of this study is to assess the role of chest US and Doppler examination in the assessment of peripheral lung lesions in comparison with chest CT, which is the gold standard for chest diagnosis. Subjects and Methods: This cross-sectional study involved 120 patients who underwent chest CT examination, full chest US, and Doppler. Results were assessed blindly and compared with CT. Results: The main lung lesions assessed by US and Doppler were lung collapse, consolidation, interstitial lung changes, and peripheral pulmonary nodules, with the estimated sensi-tivity, specificity, positive predictive value, negative predictive value, and accuracy of detection ranging from 63.9%-76.9%, 94%-95%, 79.2%-95%, 85.9%-91.5%, and 85%-93.3% re-spectively. There was a significant correlation between US and Doppler in comparison with CT. Conclusion: Chest US proved its value as a complementary tool for the diagnosis of peripheral lung lesions, with high specificity and accuracy. This finding was enforced by Doppler, history, and clinical suspicion, although the use of CT to obtain the final diagnosis would be more applicable.

Vertebral fracture (VF) locations are bimodally distributed in the spine. The association between VF and bone attenuation (BA) measured on chest CT scans varied according to the location of VFs, indicating that other factors than only BA play a role in the bimodal distribution of VFs.INTRODUCTION: Vertebral fractures (VFs) are associated with low bone mineral density but are not equally distributed throughout the spine and occur most commonly at T7-T8 and T11-T12 ("cVFs") and less commonly at T4-T6 and T9-T10 ("lcVF"). We aimed to determine whether associations between bone attenuation (BA) and VFs vary between subjects with cVFs only, with lcVFs only and with both cVFs and lcVFs.METHODS: Chest CT images of T4-T12 in 1237 smokers with and without COPD were analysed for prevalent VFs according to the method described by Genant (11,133 vertebrae). BA (expressed in Hounsfield units) was measured in all non-fractured vertebrae (available for 10,489 vertebrae). Linear regression was used to compare mean BA, and logistic regression was used to estimate the association of BA with prevalent VFs (adjusted for age and sex).RESULTS: On vertebral level, the proportion of cVFs was significantly higher than of lcVF (5.6% vs 2.0%). Compared to subjects without VFs, BA was 15% lower in subjects with cVFs (p < 0.0001), 25% lower in subjects with lcVFs (p < 0.0001) and lowest in subjects with cVFs and lcVFs (- 32%, p < 0.0001). The highest ORs for presence of VFs per - 1SD BA per vertebra were found in subjects with both cVFs and lcVFs (3.8 to 4.6).CONCLUSIONS: The association between VFs and BA differed according to VF location. ORs increased from subjects with cVFs to subjects with lcVFs and were highest in subjects with cVFs and lcVFs, indicating that other factors than only BA play a role in the bimodal VF distribution.TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT00292552.

Background: On the first of June, 2021, the total number of the registered cases of covid-19 by the Iraqi Ministry of Health was 1,201,352 , and the total number of deaths was 16375 patients. The aim of this paper is to describe chest CT-scan findings of an Iraqi patient who was observed early during June, 2021, and had symptomatic covid-19, but he didn’t need hospitalization. Patients and methods: A forty-year old school teacher developed covid-19 disease with fever, fatigue, anorexia, and cough. The patient recovered after about two weeks. Results: Chest CT-scan performed during first week of illness showed: (1) Multiple bilateral ground glass opacities. (2) Atelectatic bands. (3) Thickening of the interlobular septa. (4) Vascular thickening. Conclusion: The chest CT-scan findings in this Iraqi patient was rather typical of covid-19 disease and included the most commonly reported abnormality of ground-glass shadows

Introduction: This study aimed to define the clinical features and laboratory findings that may be associated with COVID-19 pneumonia in patients with suspected COVID-19 pneumonia who presented to the emergency department in order to be able to recognize the disease early until molecular tests are available, and thus to isolate sick people as soon as possible. Patients and Methods: We retrospectively examined patients who were evaluated with the suspicion of COVID-19 between March 2020 and August 2020. We used the patient information management system to record patient demographic and clinical features, laboratory findings, and CT results. In subgroups with and without pneumonia, we compared these characteristics in confirmed COVID-19 patients. Results: Our study included 1,277 patients who were evaluated for suspected COVID-19. COVID-19 pneumonia was detected in 71.2% (n=210) of a total of 295 (23.4%) patients with a diagnosis of COVID-19 who were confirmed by RT-PCR positivity. Although 56.2% (n=18) of those with pneumonia were men, no statistically significant relationship was detected between gender distribution and CT findings (p=0.449). When we compared the patients in 3 subgroups according to age groups, it was observed that 59.4% (n=38) of those with negative CT findings were in the 18-44 age group, and 44.8% (n=94) of those with positive CT findings were 65 and over age group, and this distribution was found to be statistically significant (p

The main purpose of this study was to compare the parameters of computed tomography (CT) and the corresponding patient doses undergoing chest CT scan examinations in different regions of Brazil, providing the current scenario of how these procedures are being carried out in the country as well as the patient dose distribution. Thirty institutions, across 17 states and the Federal District participated in the survey. The evaluation included 30 multislice CTs of seven different models, manufactured by General Electric healthcare (GE). For each institution, data from 10 adult chest CT examinations, performed without contrast, were collected remotely. The analysis of the results showed a significant difference of the CTDIvol values, ranging from 1.1 mGy to 46.6 mGy. In 7 institutions. The mean value of CTDIvol was higher than values found in the literature and the UK Reference Levels. It was also observed that, regardless of the region of the country, for the same CT model, different scanning parameters were used, which resulted in CTDIvol up to five times higher among these institutions. Repetitions of CT acquisitions and scouts with radiation field dimensions larger than the region of interest were found in 25% of chest examinations, resulting in higher absorbed doses. The results of this work show a mapping of the chest CT procedures, which enable to establish strategic plans for the country. In addition, each institution will be able to implement an appropriate optimization program and establish institutional reference levels.

Evaluating the hazards and beneficiary of the chest - CT imaging for patients of COVID-19 Subjects and Methods: - A total number of 160 suspected Covid-19 patients were classified into four groups (GP1(n=40) ,GP2(n=40) and GP3(n=40)). The groups undergoing CT chest examination (Toshiba, Activion 16) .The acquisition protocols for the different groups were as follows: GP 1 ((n=40), 100 Kv , 160 ±15 mAs , GP2 ((n=40) , 120 Kv , 150 ±20 mAs) , GP3((n=40) , were 120 Kv Sure Exposure (SE), GP4((n=40) , 100 Kv , Sure Exposure (SE)) . and the other parameters are (pitch=1.5; rotation time=0.75 sec; matrix size 512x512), All images were reconstructed by Filtered Back Projection. Results:- The CTDI volume for the different groups were: (GP1: 7.8±0.09 mGy ; GP2: 26.4 ±0.25mGy; GP3: 16.8 ±4.7mGy; GP4: 5.5±0.1mGy , P In contrast, CNR and SNR were insignificantly different (P>0.05) among all groups. GP1and GP4 has a highly significant noise (P

Purpose COVID-19 has spread rapidly worldwide since its initial appearance, creating the need for faster diagnostic methods and tools. Due to the high rate of false-negative RT-PCR tests, the role of chest CT examination has been investigated as an auxiliary procedure. The main goal of this work is to establish a well-defined strategy for 3D segmentation of the airways and lungs of COVID-19 positive patients from CT scans, including detected abnormalities. Their identification and the volumetric quantification could allow an easier classification in terms of gravity, extent and progression of the infection. Moreover, these 3D reconstructions can provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia. Methods Segmentation process was performed utilizing a proprietary software, starting from six different stacks of chest CT images of subjects with and without COVID-19. In this context, a comparison between manual and automatic segmentation methods of the respiratory system was conducted, to assess the potential value of both techniques, in terms of time consumption, required anatomical knowledge and branch detection, in healthy and pathological conditions. Results High-quality 3D models were obtained. They can be utilized to assess the impact of the pathology, by volumetrically quantifying the extension of the affected areas. Indeed, based on the obtained reconstructions, an attempted classification for each patient in terms of the severity of the COVID-19 infection has been outlined. Conclusions Automatic algorithms allowed for a substantial reduction in segmentation time. However, a great effort was required for the manual identification of COVID-19 CT manifestations. The developed automated procedure succeeded in obtaining sufficiently accurate models of the airways and the lungs of both healthy patients and subjects with confirmed COVID-19, in a reasonable time.

Computed tomography (CT) of the chest is one of the main diagnositic tools for coronavirus disease 2019 (COVID-19) infection. To document the chest CT findings in patients with confirmed COVID-19 and their association with the clinical severity, we searched related literatures through PubMed, MEDLINE, Embase, Web of Science (inception to May 4, 2020) and reviewed reference lists of previous systematic reviews. A total of 31 case reports (3768 patients) on CT findings of COVID-19 were included. The most common comorbid conditions were hypertension (18.4%) and diabetes mellitus (8.3%). The most common symptom was fever (78.7%), followed by cough (60.2%). It took an average of 5.6 days from symptom onset to admission. The most common chest CT finding was vascular enlargement (84.8%), followed by ground-glass opacity (GGO) (60.1%), air-bronchogram (47.8%), and consolidation (41.4%). Most lung lesions were located in the lung periphery (72.2%) and involved bilateral lung (76%). Most patients showed normal range of laboratory findings such as white blood cell count (96.4%) and lymphocyte (87.2%). Compared to previous published meta-analyses, our study is the first to summarize the different radiologic characteristics of chest CT in a total of 3768 COVID-19 patients by compiling case series studies. A comprehensive diagnostic approach should be adopted for patients with known COVID-19, suspected cases, and for exposed individuals.