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1.
Front Biosci (Landmark Ed) ; 26(12): 1607-1612, 2021 12 30.
Article in English | MEDLINE | ID: covidwho-1614663

ABSTRACT

PURPOSE: The aim of this observational study was to highlight high resolution CT scan characteristics of COVID-19-associated pulmonary aspergillosis (CAPA) with a focus on the detection of de-novo appeared or evolved bronchiectasis. METHODS: From March 2020 to May 2021, we enrolled 350 consecutive mechanically ventilated ICU patients with COVID-19. Patients with CAPA and at least one chest CT scan performed within 15 days from the diagnosis were included. Two radiologists were asked to identify typical and atypical signs of COVID-19 pneumonia. Bronchiectasis locations were described and a modified Reiff score was calculated, as severity score. A total of 19 CAPA patients (median age 71.0, Interquartile range (IQR) 62.5-75.0; male 16, 84.2%) were included. RESULTS: According to the 2020 ECMM/ISHAM criteria, 18 patients had probable CAPA and one had proven CAPA. The median time between hospital admission and CT scan was 21 days (IQR 14.5-25.0). The incidence of bronchiectasis in the study population was 57.9% (n = 11). Tubular bronchiectasis was detected in 10 patients and were scored as follows: three patients had a score of 1, three patients had a score of score 2, one patient had a score of 5 and four patients had a score of 6. Eight patients had a previous CT scan (performed at hospital admission), among them: 5 patients developed de-novo bronchiectasis, while 2 patients demonstrated a volumetric increase of bronchiectasis. At the 6-months follow-up, the mortality rate for patients with CAPA was >60%. CONCLUSION: the radiologic detection of de-novo appearance or volumetric increase of bronchiectasis in COVID-19 should lead clinicians to search for fungal superinfections.


Subject(s)
Bronchiectasis , COVID-19 , Invasive Pulmonary Aspergillosis , Pulmonary Aspergillosis , Aged , Bronchiectasis/diagnostic imaging , Humans , Male , SARS-CoV-2 , Tomography , Tomography, X-Ray Computed
2.
Medicine (Baltimore) ; 100(47): e27980, 2021 Nov 24.
Article in English | MEDLINE | ID: covidwho-1604285

ABSTRACT

RATIONALE: Pulmonary fibrosis is an infamous sequela of coronavirus disease 2019 (COVID-19) pneumonia leading to long-lasting respiratory problems and activity limitations. Pulmonary rehabilitation is beneficial to improve the symptoms of lung fibrosis. We experienced a post-COVID-19 pulmonary fibrosis patient who received a structured exercise-based pulmonary rehabilitation program. PATIENT CONCERNS: This article presents a case of successful pulmonary rehabilitation of a patient with post-COVID-19 pulmonary fibrosis. The patient could not cut off the oxygen supplement even after a successful recovery from COVID-19. DIAGNOSIS: Diagnosis of COVID-19 was based on the reverse transcription-polymerase chain reaction (RT-PCR). Pulmonary fibrosis was diagnosed by patient's complaint, clinical appearance, and computed tomography (CT) on chest. INTERVENTION: The patient underwent ten sessions of exercise-based rehabilitation program according to Consensus Document on Pulmonary Rehabilitation in Korea, 2015. OUTCOME: On the 8th day, he could cut off the oxygen supplementation and complete the one-hour exercise without oxygen. He was discharged after completing the 10-session program without any activity limitations. LESSONS: Exercise-based pulmonary rehabilitation will help the post-COVID-19 pulmonary fibrosis patients. This case suggested the importance of pulmonary rehabilitation program to the post-COVID-19 pulmonary fibrosis patient.


Subject(s)
COVID-19/complications , Lung/diagnostic imaging , Pulmonary Fibrosis/rehabilitation , COVID-19/diagnosis , COVID-19 Testing , Humans , Lung/pathology , Male , Middle Aged , Oxygen , Pulmonary Fibrosis/diagnostic imaging , Pulmonary Fibrosis/etiology , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Tomography, X-Ray Computed
3.
BMC Pulm Med ; 22(1): 1, 2022 Jan 03.
Article in English | MEDLINE | ID: covidwho-1608729

ABSTRACT

BACKGROUND: Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS: This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS: Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS: Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Amides/therapeutic use , Antiviral Agents/therapeutic use , Body Temperature , C-Reactive Protein/metabolism , COVID-19/drug therapy , COVID-19/physiopathology , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , L-Lactate Dehydrogenase/blood , Lung/diagnostic imaging , Lymphocyte Count , Male , Middle Aged , Mucin-1/blood , Neutrophils , Predictive Value of Tests , Prognosis , Pyrazines/therapeutic use , Radiographic Image Interpretation, Computer-Assisted , Retrospective Studies , SARS-CoV-2 , Steroids/therapeutic use
4.
Curr Med Imaging ; 17(12): 1487-1495, 2021.
Article in English | MEDLINE | ID: covidwho-1595319

ABSTRACT

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.


Subject(s)
COVID-19 , Tomography, X-Ray Computed , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/physiopathology , Retrospective Studies , SARS-CoV-2
5.
Dis Markers ; 2021: 7686374, 2021.
Article in English | MEDLINE | ID: covidwho-1595046

ABSTRACT

Objective: S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are indicators of global transmethylation and may play an important role as markers of severity of COVID-19. Methods: The levels of plasma SAM and SAH were determined in patients admitted with COVID-19 (n = 56, mean age = 61). Lung injury was identified by computed tomography (CT) in accordance with the CT0-4 classification. Results: SAM was found to be a potential marker of lung damage risk in COVID-19 patients (SAM > 80 nM; CT3,4 vs. CT 0-2: relative ratio (RR) was 3.0; p = 0.0029). SAM/SAH > 6.0 was also found to be a marker of lung injury (CT2-4 vs. CT0,1: RR = 3.47, p = 0.0004). There was a negative association between SAM and glutathione level (ρ = -0.343, p = 0.011). Interleukin-6 (IL-6) levels were associated with SAM (ρ = 0.44, p = 0.01) and SAH (ρ = 0.534, p = 0.001) levels. Conclusions: A high SAM level and high methylation index are associated with the risk of lung injury in patients with COVID-19. The association of SAM with IL-6 and glutathione indicates an important role of transmethylation in the development of cytokine imbalance and oxidative stress in patients with COVID-19.


Subject(s)
COVID-19/complications , Lung Injury/blood , S-Adenosylhomocysteine/blood , S-Adenosylmethionine/blood , SARS-CoV-2 , Adult , Aged , Aged, 80 and over , Atherosclerosis/epidemiology , Biomarkers , COVID-19/epidemiology , Comorbidity , Diabetes Mellitus/epidemiology , Female , Glutathione/blood , Humans , Hypertension/epidemiology , Interleukin-6/blood , Lung Injury/diagnostic imaging , Lung Injury/etiology , Male , Methylation , Middle Aged , Military Personnel , Risk , Tomography, X-Ray Computed , Young Adult
6.
Sci Rep ; 11(1): 24065, 2021 12 15.
Article in English | MEDLINE | ID: covidwho-1585806

ABSTRACT

COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.


Subject(s)
COVID-19/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , COVID-19 Testing/methods , Deep Learning , Heuristics , Humans , Neural Networks, Computer , Tomography, X-Ray Computed
7.
J Acoust Soc Am ; 150(6): 4118, 2021 12.
Article in English | MEDLINE | ID: covidwho-1583239

ABSTRACT

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.


Subject(s)
COVID-19 , Humans , Image Processing, Computer-Assisted , Lung/diagnostic imaging , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
8.
Behav Neurol ; 2021: 2560388, 2021.
Article in English | MEDLINE | ID: covidwho-1582890

ABSTRACT

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , Computers , Humans , SARS-CoV-2 , Tomography, X-Ray Computed
9.
Tuberk Toraks ; 69(4): 486-491, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1580010

ABSTRACT

Introduction: Computed tomography (CT) is an auxiliary modality in the diagnosis of the novel Coronavirus (COVID-19) disease and can guide physicians in the presence of lung involvement. In this study, we aimed to investigate the contribution of deep learning to diagnosis in patients with typical COVID-19 pneumonia findings on CT. Materials and Methods: This study retrospectively evaluated 690 lesions obtained from 35 patients diagnosed with COVID-19 pneumonia based on typical findings on non-contrast high-resolution CT (HRCT) in our hospital. The diagnoses of the patients were also confirmed by other necessary tests. HRCT images were assessed in the parenchymal window. In the images obtained, COVID-19 lesions were detected. For the deep Convolutional Neural Network (CNN) algorithm, the Confusion matrix was used based on a Tensorflow Framework in Python. Result: A total of 596 labeled lesions obtained from 224 sections of the images were used for the training of the algorithm, 89 labeled lesions from 27 sections were used in validation, and 67 labeled lesions from 25 images in testing. Fifty-six of the 67 lesions used in the testing stage were accurately detected by the algorithm while the remaining 11 were not recognized. There was no false positive. The Recall, Precision and F1 score values in the test group were 83.58, 1, and 91.06, respectively. Conclusions: We successfully detected the COVID-19 pneumonia lesions on CT images using the algorithms created with artificial intelligence. The integration of deep learning into the diagnostic stage in medicine is an important step for the diagnosis of diseases that can cause lung involvement in possible future pandemics.


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
10.
Tuberk Toraks ; 69(4): 492-498, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1580009

ABSTRACT

Introduction: To date, there is limited data on the long-term changes in the lungs of patients recovering from coronavirus (COVID-19) pneumonia. In order to evaluate pulmonary sequelae, it was planned to investigate fibrotic changes observed as sequelae in lung tissue in 3-6-month control thorax computerized tomography (CT) scans of moderate-to-severe COVID-19 pneumonia survivors. Materials and Methods: A total of 84 patients (mean age: 67.3 years ±15) with moderate-to-severe pneumonia on chest tomography at the time of diagnosis were included in the study, of which 51 (61%) were males and 33 (39%) were females. Initial and follow-up CT scans averaged 8.3 days ± 2.2 and 112.1 days ± 14.6 after symptom onset, respectively. Participants were recorded in two groups as those with and without fibrotic-like changes such as traction bronchiectasis, fibrotic - parenchymal bands, honeycomb appearance according to 3-6 months follow-up CT scans. Differences between the groups were evaluated with a two-sampled t-test. Logistic regression analyzes were performed to determine independent predictive factors of fibrotic-like sequelae changes. Result: On follow-up CTs, fibrotic-like changes were observed in 29 (35%) of the 84 participants (Group 1), while the remaining 55 (65%) showed complete radiological recovery (Group 2). With logistic regression analysis, hospital stay of 22 days or longer (OR: 4.9; 95% CI: 20, 32; p< 0.05) and a CT score of 15 or more at diagnosis (OR: 2.2; 95% CI: 13.5, 18; p< 0.05) were found to be an independent predictor for sequelae fibrotic changes in lung tissue. Conclusions: More than one-third of patients who survived COVID-19 pneumonia had fibrotic-like sequelae changes in the lung parenchyma. These changes were found to be associated with the presence of severe pneumonia at the time of diagnosis and longer hospital stay.


Subject(s)
COVID-19 , Pneumonia , Aged , Female , Follow-Up Studies , Humans , Lung/diagnostic imaging , Male , Pneumonia/diagnostic imaging , Retrospective Studies , SARS-CoV-2 , Thorax , Tomography, X-Ray Computed
11.
Ann Med ; 53(1): 337-344, 2021 12.
Article in English | MEDLINE | ID: covidwho-1575678

ABSTRACT

BACKGROUND: To minimise the risk of COVID-19 transmission, an ambulant screening protocol for COVID-19 in patients before admission to the hospital was implemented, combining the SARS CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR) on a nasopharyngeal swab, a chest computed tomography (CT) and assessment of clinical symptoms. The aim of this study was to evaluatethe diagnostic yield and the proportionality of this pre-procedural screeningprotocol. METHODS: In this mono-centre, prospective, cross-sectional study, all patients admitted to the hospital between 22nd April 2020 until 14th May 2020 for semi-urgent surgery, haematological or oncological treatment, or electrophysiological investigationunderwent a COVID-19 screening 2 days before their procedure. At a 2-week follow-up, the presence of clinical symptoms was evaluated by telephone as a post-hoc evaluation of the screening approach.Combined positive RT-PCR assay and/or positive chest CT was used as gold standard. Post-procedural outcomes of all patients diagnosed positive for COVID-19 were assessed. RESULTS: In total,528 patients were included of which 20 (3.8%) were diagnosed as COVID-19 positive and 508 (96.2%) as COVID-19 negative. 11 (55.0%) of COVID-19 positive patients had only a positive RT-PCR assay, 3 (15.0%) had only a positive chest CT and 6 (30%) had both a positive RT-PCR assay and chest CT. 10 out of 20 (50.0%) COVID-19 positive patients reported no single clinical symptom at the screening. At 2 week follow-up, 50% of these patients were still asymptomatic. 37.5% of all COVID-19 negative patients were symptomatic at screening. In the COVID-19 negative group without symptoms at screening, 78 (29.3%) patients developed clinical symptoms at a 2-week follow-up. CONCLUSION: This study suggests that routine chest CT and assessment of self-reported symptoms have limited value in the preprocedural COVID-19 screening due to low sensitivity and/or specificity.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Mass Screening/methods , Patient Admission , Adult , Aged , COVID-19/epidemiology , Cross-Sectional Studies , Female , Follow-Up Studies , Humans , Male , Middle Aged , Prospective Studies , Reverse Transcriptase Polymerase Chain Reaction , Sensitivity and Specificity , Tomography, X-Ray Computed
12.
Clin Imaging ; 77: 151-157, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1573759

ABSTRACT

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.


Subject(s)
COVID-19 , Pandemics , Artificial Intelligence , Biomarkers , Humans , Lung/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed
13.
Ann Med ; 53(1): 295-301, 2021 12.
Article in English | MEDLINE | ID: covidwho-1575822

ABSTRACT

INTRODUCTION: Critically ill patients with COVID-19 are at increased risk of developing a hypercoagulable state due to haemostatic changes directly related to the SARS-CoV-2 infection or to the consequence of the cytokine storm. Anticoagulation is now recommended to reduce the thrombotic risk. Ilio-psoas haematoma (IPH) is a potentially lethal condition that can arise during the hospitalization, especially in intensive care units (ICUs) and frequently reported as a complication of anticoagulation treatment. MATERIALS AND METHODS: We report a case series of seven subjects with SARS-CoV-2 pneumonia complicated by Ilio-psoas haematomas (IPHs) at our COVID-Hospital in Rome, Italy. RESULTS: Over the observation period, 925 subjects with confirmed SARS-CoV-2 infection were admitted to our COVID-hospital. Among them, we found seven spontaneous IPHs with an incidence of 7.6 cases per 1000 hospitalization. All the reported cases had a severe manifestation of COVID-19 pneumonia, with at least one comorbidity and 5/7 were on treatment with low weight molecular heparin for micro or macro pulmonary thrombosis. CONCLUSIONS: Given the indications to prescribe anticoagulant therapy in COVID-19 and the lack of solid evidences on the optimal dose and duration, it is important to be aware of the iliopsoas haematoma as a potentially serious complication in COVID-19 inpatients. KEY MESSAGE Critically ill patients with COVID-19 are at increased risk of hypercoagulability state and anticoagulation therapy is recommended. Ilio-psoas haematoma (IPH) is found to be a complication of anticoagulation regimen especially in severe COVID-19 cases. An incidence of 7.6 cases per 1000 admission of IPHs was reported. Hypoesthesia of the lower limbs, pain triggered by femoral rotation, hypovolaemia and anaemia are the most common symptoms and signs of IPHs that should alert physician.


Subject(s)
Anticoagulants/adverse effects , COVID-19/complications , Hematoma/epidemiology , Psoas Muscles/diagnostic imaging , Thrombophilia/drug therapy , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/drug therapy , COVID-19/virology , Critical Illness/mortality , Critical Illness/therapy , Female , Glucocorticoids/therapeutic use , Hematoma/chemically induced , Hematoma/diagnosis , Hematoma/drug therapy , Heparin, Low-Molecular-Weight , Hospital Mortality , Humans , Incidence , Intensive Care Units , Italy/epidemiology , Magnetic Resonance Imaging , Male , Middle Aged , Muscular Diseases , SARS-CoV-2/isolation & purification , SARS-CoV-2/pathogenicity , Severity of Illness Index , Thrombophilia/etiology , Tomography, X-Ray Computed , Treatment Outcome
14.
IEEE J Biomed Health Inform ; 25(11): 4110-4118, 2021 11.
Article in English | MEDLINE | ID: covidwho-1570200

ABSTRACT

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N = 869 patients from two sites (D1: N = 822, D2: N = 47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D1train (N = 606) and 30% test-set (Dtest: D1test (N = 216) + D2 (N = 47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D1train, (D1train_sub, N = 88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D1train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0.81 on Dtest and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p<0.001). ClAIN improved the performance over AIP yielding an AUC of 0.84 (p = 0.04) on Dtest. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Lung , Nomograms , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed , Ventilators, Mechanical
15.
BMC Med Imaging ; 21(1): 192, 2021 12 13.
Article in English | MEDLINE | ID: covidwho-1571744

ABSTRACT

AIM: This study is to compare the lung image quality between shelter hospital CT (CT Ark) and ordinary CT scans (Brilliance 64) scans. METHODS: The patients who received scans with CT Ark or Brilliance 64 CT were enrolled. Their lung images were divided into two groups according to the scanner. The objective evaluation methods of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used. The subjective evaluation methods including the evaluation of the fine structure under the lung window and the evaluation of the general structure under the mediastinum window were compared. Kappa method was used to assess the reliability of the subjective evaluation. The subjective evaluation results were analyzed using the Wilcoxon rank sum test. SNR and CNR were tested using independent sample t tests. RESULTS: There was no statistical difference in somatotype of enrolled subjects. The Kappa value between the two observers was between 0.68 and 0.81, indicating good consistency. For subjective evaluation results, the rank sum test P value of fine structure evaluation and general structure evaluation by the two observers was ≥ 0.05. For objective evaluation results, SNR and CNR between the two CT scanners were significantly different (P<0.05). Notably, the absolute values ​​of SNR and CNR of the CT Ark were larger than Brilliance 64 CT scanner. CONCLUSION: CT Ark is fully capable of scanning the lungs of the COVID-19 patients during the epidemic in the shelter hospital.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Mobile Health Units/standards , Tomography, X-Ray Computed/instrumentation , Tomography, X-Ray Computed/standards , Adult , Aged , COVID-19/epidemiology , China/epidemiology , Female , Humans , Male , Middle Aged , Observer Variation , Pandemics , SARS-CoV-2 , Signal-To-Noise Ratio
16.
J Laryngol Otol ; 135(11): 1010-1018, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1569186

ABSTRACT

OBJECTIVE: The primary goal of this study was to evaluate the association between olfactory dysfunction or taste impairment and disease severity and radiological findings in coronavirus disease-2019. The secondary goal was to assess the prevalence, severity and course of olfactory dysfunction or taste impairment in patients with coronavirus disease 2019. METHOD: This prospective observational cohort study evaluated patients hospitalised with coronavirus disease 2019 between April 1 and 1 May 2020. Olfactory dysfunction and taste impairment were evaluated by two questionnaires. Chest computed tomography findings and coronavirus disease-2019 severity were assessed. RESULTS: Among 133 patients, 23.3 per cent and 30.8 per cent experienced olfactory dysfunction and taste impairment, respectively, and 17.2 per cent experienced both. The mean age was 56.03 years, and 64.7 per cent were male and 35.3 per cent were female. No statistically significant association was found between olfactory dysfunction (p = 0.706) and taste impairment (p = 0.35) with either disease severity or chest computed tomography grading. CONCLUSION: Olfactory dysfunction or taste impairment does not have prognostic importance in patients with coronavirus disease 2019.


Subject(s)
COVID-19/complications , Olfaction Disorders/epidemiology , SARS-CoV-2 , Severity of Illness Index , Taste Disorders/epidemiology , Adult , Aged , COVID-19/diagnostic imaging , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Olfaction Disorders/virology , Prevalence , Prognosis , Prospective Studies , Taste Disorders/virology , Tomography, X-Ray Computed
17.
Phys Med Biol ; 66(24)2021 12 31.
Article in English | MEDLINE | ID: covidwho-1569504

ABSTRACT

Objective.Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.Approach.A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.Main Results.Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.Significance.The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.


Subject(s)
COVID-19 , Humans , Lung , SARS-CoV-2 , Thorax , Tomography, X-Ray Computed
18.
Br J Radiol ; 95(1129): 20210759, 2022 Jan 01.
Article in English | MEDLINE | ID: covidwho-1566545

ABSTRACT

OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test. METHODS: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort. RESULTS: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or "COVID-19 without virus detection", as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%. CONCLUSION: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making. ADVANCES IN KNOWLEDGE: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Adult , Aged , COVID-19/diagnosis , COVID-19/pathology , False Negative Reactions , False Positive Reactions , Female , Humans , Image Processing, Computer-Assisted , Male , Radiography, Thoracic , Reproducibility of Results , Retrospective Studies , Sensitivity and Specificity , Severity of Illness Index , Tomography, X-Ray Computed
19.
Br J Radiol ; 95(1129): 20210570, 2022 Jan 01.
Article in English | MEDLINE | ID: covidwho-1566544

ABSTRACT

OBJECTIVE: Multisystem inflammatory syndrome in children (MIS-C) is seen as a serious delayed complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The aim of this study was to describe the most common imaging features of MIS-C associated with SARS-CoV-2. METHODS: A retrospective review was made of the medical records and radiological imaging studies of 47 children (26 male, 21 female) in the age range of 25 months-15 years who were diagnosed with MIS-C between August 2020 and March 2021. Chest radiographs were available for all 47 patients, thorax ultrasound for 6, chest CT for 4, abdominal ultrasound for 42, abdomen CT for 9, neck ultrasound for 4, neck CT for 2, brain CT for 1, and brain MRI for 3. RESULTS: The most common finding on chest radiographs was perihilar-peribronchial thickening (46%). The most common findings on abdominal ultrasonography were mesenteric inflammation (42%), and hepatosplenomegaly (38%, 28%). Lymphadenopathy was determined in four patients who underwent neck ultrasound, one of whom had deep neck infection on CT. One patient had restricted diffusion and T2 hyperintensity involving the corpus callosum splenium on brain MRI, and one patient had epididymitis related with MIS-C. CONCLUSION: Pulmonary manifestations are uncommon in MIS-C. In the abdominal imaging, mesenteric inflammation, hepatosplenomegaly, periportal edema, ascites and bowel wall thickening are the most common findings. ADVANCES IN KNOWLEDGE: The imaging findings of MIS-C are non-specific and can mimic many other pathologies. Radiologists should be aware that these findings may indicate the correct diagnosis of MIS-C.


Subject(s)
COVID-19/complications , Systemic Inflammatory Response Syndrome/diagnostic imaging , Adolescent , Brain/diagnostic imaging , COVID-19/diagnostic imaging , Child , Child, Preschool , Female , Humans , Magnetic Resonance Imaging , Male , Neck/diagnostic imaging , Neuroimaging , Radiography, Abdominal , Radiography, Thoracic , Retrospective Studies , Tomography, X-Ray Computed , Ultrasonography
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