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1.
BMC Pregnancy Childbirth ; 21(1): 658, 2021 Sep 28.
Article in English | MEDLINE | ID: covidwho-1770502

ABSTRACT

BACKGROUND: Whilst the impact of Covid-19 infection in pregnant women has been examined, there is a scarcity of data on pregnant women in the Middle East. Thus, the aim of this study was to examine the impact of Covid-19 infection on pregnant women in the United Arab Emirates population. METHODS: A case-control study was carried out to compare the clinical course and outcome of pregnancy in 79 pregnant women with Covid-19 and 85 non-pregnant women with Covid-19 admitted to Latifa Hospital in Dubai between March and June 2020. RESULTS: Although Pregnant women presented with fewer symptoms such as fever, cough, sore throat, and shortness of breath compared to non-pregnant women; yet they ran a much more severe course of illness. On admission, 12/79 (15.2%) Vs 2/85 (2.4%) had a chest radiograph score [on a scale 1-6] of ≥3 (p-value = 0.0039). On discharge, 6/79 (7.6%) Vs 1/85 (1.2%) had a score ≥3 (p-value = 0.0438). They also had much higher levels of laboratory indicators of severity with values above reference ranges for C-Reactive Protein [(28 (38.3%) Vs 13 (17.6%)] with p < 0.004; and for D-dimer [32 (50.8%) Vs 3(6%)]; with p < 0.001. They required more ICU admissions: 10/79 (12.6%) Vs 1/85 (1.2%) with p=0.0036; and suffered more complications: 9/79 (11.4%) Vs 1/85 (1.2%) with p=0.0066; of Covid-19 infection, particularly in late pregnancy. CONCLUSIONS: Pregnant women presented with fewer Covid-19 symptoms but ran a much more severe course of illness compared to non-pregnant women with the disease. They had worse chest radiograph scores and much higher levels of laboratory indicators of disease severity. They had more ICU admissions and suffered more complications of Covid-19 infection, such as risk for miscarriage and preterm deliveries. Pregnancy with Covid-19 infection, could, therefore, be categorised as high-risk pregnancy and requires management by an obstetric and medical multidisciplinary team.


Subject(s)
COVID-19 , Intensive Care Units/statistics & numerical data , Pregnancy Complications, Infectious , Premature Birth , Radiography, Thoracic , Symptom Assessment , Abortion, Spontaneous/epidemiology , Abortion, Spontaneous/etiology , C-Reactive Protein/analysis , COVID-19/blood , COVID-19/epidemiology , COVID-19/therapy , COVID-19/transmission , Case-Control Studies , Female , Fibrin Fibrinogen Degradation Products/analysis , Humans , Infant, Newborn , Infectious Disease Transmission, Vertical/prevention & control , Male , Pregnancy , Pregnancy Complications, Infectious/epidemiology , Pregnancy Complications, Infectious/physiopathology , Pregnancy Complications, Infectious/therapy , Pregnancy Complications, Infectious/virology , Pregnancy Outcome/epidemiology , Pregnancy, High-Risk , Premature Birth/epidemiology , Premature Birth/etiology , Radiography, Thoracic/methods , Radiography, Thoracic/statistics & numerical data , SARS-CoV-2/isolation & purification , Severity of Illness Index , Symptom Assessment/methods , Symptom Assessment/statistics & numerical data , United Arab Emirates/epidemiology
2.
PLoS One ; 17(3): e0265691, 2022.
Article in English | MEDLINE | ID: covidwho-1770752

ABSTRACT

Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance automated disease detection. We evaluate this hypothesis using a custom ensemble of convolutional neural network models, which we call DeBoNet, that suppresses bones in frontal CXRs. First, we train and evaluate variants of U-Nets, Feature Pyramid Networks, and other proposed custom models using a private collection of CXR images and their bone-suppressed counterparts. The DeBoNet, constructed using the top-3 performing models, outperformed the individual models in terms of peak signal-to-noise ratio (PSNR) (36.7977±1.6207), multi-scale structural similarity index measure (MS-SSIM) (0.9848±0.0073), and other metrics. Next, the best-performing bone-suppression model is applied to CXR images that are pooled from several sources, showing no abnormality and other findings consistent with COVID-19. The impact of bone suppression is demonstrated by evaluating the gain in performance in detecting pulmonary abnormality consistent with COVID-19 disease. We observe that the model trained on bone-suppressed CXRs (MCC: 0.9645, 95% confidence interval (0.9510, 0.9780)) significantly outperformed (p < 0.05) the model trained on non-bone-suppressed images (MCC: 0.7961, 95% confidence interval (0.7667, 0.8255)) in detecting findings consistent with COVID-19 indicating benefits derived from automatic bone suppression on disease classification. The code is available at https://github.com/sivaramakrishnan-rajaraman/Bone-Suppresion-Ensemble.


Subject(s)
COVID-19 , Lung Diseases , COVID-19/diagnostic imaging , Humans , Neural Networks, Computer , Radiography, Thoracic/methods , Ribs , Signal-To-Noise Ratio
3.
Radiology ; 295(3): 200463, 2020 06.
Article in English | MEDLINE | ID: covidwho-1723927

ABSTRACT

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung Diseases/diagnostic imaging , Lung Diseases/virology , Pneumonia, Viral/diagnostic imaging , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Lung/virology , Lung Diseases/pathology , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Radiography, Thoracic/methods , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Young Adult
4.
Sci Rep ; 12(1): 1847, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671622

ABSTRACT

Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 lesions and the similarity between the lesions and normal tissues. This work aims to segment lesions of different scales and lesion boundaries correctly by utilizing multiscale and multilevel features. A novel multiscale dilated convolutional network (MSDC-Net) is proposed against the scale difference of lesions and the low contrast between lesions and normal tissues in CT images. In our MSDC-Net, we propose a multiscale feature capture block (MSFCB) to effectively capture multiscale features for better segmentation of lesions at different scales. Furthermore, a multilevel feature aggregate (MLFA) module is proposed to reduce the information loss in the downsampling process. Experiments on the publicly available COVID-19 CT Segmentation dataset demonstrate that the proposed MSDC-Net is superior to other existing methods in segmenting lesion boundaries and large, medium, and small lesions, and achieves the best results in Dice similarity coefficient, sensitivity and mean intersection-over-union (mIoU) scores of 82.4%, 81.1% and 78.2%, respectively. Compared with other methods, the proposed model has an average improvement of 10.6% and 11.8% on Dice and mIoU. Compared with the existing methods, our network achieves more accurate segmentation of lesions at various scales and lesion boundaries, which will facilitate further clinical analysis. In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Neural Networks, Computer , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Datasets as Topic , Female , Humans , Imaging, Three-Dimensional/methods , Male
5.
Sci Rep ; 11(1): 8304, 2021 04 15.
Article in English | MEDLINE | ID: covidwho-1545653

ABSTRACT

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .


Subject(s)
COVID-19/diagnostic imaging , Neural Networks, Computer , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , COVID-19/virology , Datasets as Topic , Humans , SARS-CoV-2/isolation & purification
6.
Sci Rep ; 11(1): 23210, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1545637

ABSTRACT

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.


Subject(s)
COVID-19/complications , Deep Learning , Expert Systems , Image Processing, Computer-Assisted/methods , Pneumonia/diagnosis , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Algorithms , COVID-19/virology , Humans , Incidence , India/epidemiology , Neural Networks, Computer , Pneumonia/diagnostic imaging , Pneumonia/epidemiology , Pneumonia/virology , Retrospective Studies , SARS-CoV-2/isolation & purification
7.
BMC Cardiovasc Disord ; 21(1): 522, 2021 10 29.
Article in English | MEDLINE | ID: covidwho-1486551

ABSTRACT

BACKGROUND: With the high prevalence of COVID-19 infections worldwide, the multisystem inflammatory syndrome in adults (MIS-A) is becoming an increasingly recognized entity. This syndrome presents in patients several weeks after infection with COVID-19 and is associated with thrombosis, elevated inflammatory markers, hemodynamic compromise and cardiac dysfunction. Treatment is often with steroids and intravenous immunoglobulin (IVIg). The pathologic basis of myocardial injury in MIS-A, however, is not well characterized. In our case report, we obtained endomyocardial biopsy that revealed a pattern of myocardial injury similar to that found in COVID-19 cardiac specimens. CASE PRESENTATION: A 26-year-old male presented with fevers, chills, headache, nausea, vomiting, and diarrhea 5 weeks after his COVID-19 infection. His SARS-CoV-2 PCR was negative and IgG was positive, consistent with prior infection. He was found to be in cardiogenic shock with biventricular failure, requiring inotropes and diuretics. Given concern for acute fulminant myocarditis, an endomyocardial biopsy (EMB) was performed, showing an inflammatory infiltrate consisting predominantly of interstitial macrophages with scant T lymphocytes. The histologic pattern was similar to that of cardiac specimens from COVID-19 patients, helping rule out myocarditis as the prevailing diagnosis. His case was complicated by persistent hypoxemia, and a computed tomography scan revealed pulmonary emboli. He received IVIg, steroids, and anticoagulation with rapid recovery of biventricular function. CONCLUSIONS: MIS-A should be considered as the diagnosis in patients presenting several weeks after COVID-19 infection with severe inflammation and multi-organ involvement. In our case, EMB facilitated identification of MIS-A and guided therapy. The patient's biventricular function recovered with IVIg and steroids.


Subject(s)
Anticoagulants/administration & dosage , COVID-19 , Myocarditis/diagnosis , Shock, Cardiogenic , Systemic Inflammatory Response Syndrome , Adult , Biopsy/methods , COVID-19/complications , COVID-19/diagnosis , COVID-19/drug therapy , COVID-19/immunology , COVID-19/physiopathology , Cardiotonic Agents/administration & dosage , Diagnosis, Differential , Diuretics/administration & dosage , Electrocardiography/methods , Humans , Immunoglobulins, Intravenous/administration & dosage , Male , Myocardium/pathology , Radiography, Thoracic/methods , SARS-CoV-2 , Shock, Cardiogenic/diagnosis , Shock, Cardiogenic/drug therapy , Shock, Cardiogenic/etiology , Shock, Cardiogenic/physiopathology , Systemic Inflammatory Response Syndrome/diagnosis , Systemic Inflammatory Response Syndrome/drug therapy , Systemic Inflammatory Response Syndrome/physiopathology , Treatment Outcome
8.
Sci Rep ; 11(1): 20384, 2021 10 14.
Article in English | MEDLINE | ID: covidwho-1469995

ABSTRACT

Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs. The algorithm is prospectively validated in 3289 CXRs acquired from patients presenting to ED with symptoms of COVID-19 across four sites in NHS Greater Glasgow and Clyde. CovIx achieves area under receiver operating characteristic curve for COVID-19 of 0.86, with sensitivity and F1-score up to 0.83 and 0.71 respectively, and performs on-par with four board-certified radiologists. AI-based algorithms can identify CXRs with COVID-19 associated pneumonia, as well as distinguish non-COVID pneumonias in symptomatic patients presenting to ED. Pre-trained models and inference scripts are freely available at https://github.com/beringresearch/bravecx-covid .


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Radiography, Thoracic/methods , Algorithms , Artificial Intelligence , COVID-19 Testing/methods , Emergency Service, Hospital , Humans , Neural Networks, Computer , Prospective Studies , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
10.
Int J Med Sci ; 18(15): 3395-3402, 2021.
Article in English | MEDLINE | ID: covidwho-1409696

ABSTRACT

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.


Subject(s)
COVID-19/diagnostic imaging , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , COVID-19/blood , Humans , Lung/diagnostic imaging , Lung Diseases/diagnostic imaging , Lymphocyte Count , Oxygen/therapeutic use , Prognosis
11.
Sci Prog ; 104(3): 368504211016204, 2021.
Article in English | MEDLINE | ID: covidwho-1369464

ABSTRACT

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients (p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs (p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.


Subject(s)
COVID-19/diagnostic imaging , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Adult , Aged , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Radiography, Thoracic/standards , Tomography, X-Ray Computed/standards
12.
AJR Am J Roentgenol ; 217(4): 883-887, 2021 10.
Article in English | MEDLINE | ID: covidwho-1365500

ABSTRACT

OBJECTIVE. To reduce staff exposure to infection and maintain operational efficiency, we have developed a protocol to image patients using portable chest radiography through the glass of an isolation room. This technique is safe and easy to implement. Images are of comparable quality to standard portable radiographs. CONCLUSION. This protocol, used routinely by our department during the COVID-19 pandemic, can be applied to any situation in which the patient is placed in isolation.


Subject(s)
COVID-19/diagnostic imaging , Patient Isolation/methods , Point-of-Care Systems , Radiography, Thoracic/methods , COVID-19/prevention & control , Humans , Lung/diagnostic imaging , Pandemics , SARS-CoV-2
13.
Respir Investig ; 59(6): 871-875, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1364443

ABSTRACT

Spirometry is a crucial test used in the diagnosis and monitoring of patients with chronic obstructive pulmonary disease (COPD). Severe acute respiratory syndrome coronavirus 2 pandemic has posed numerous challenges in performing spirometry. Dynamic-ventilatory digital radiography (DR) provides sequential chest radiography images during respiration with lower doses of radiation than conventional X-ray fluoroscopy and computed tomography. Recent studies revealed that parameters obtained from dynamic DR are promising for evaluating pulmonary function of COPD patients. We report two cases of COPD evaluated by dynamic-ventilatory DR for pulmonary function and treatment efficacy and discuss the potential of dynamic DR for evaluating COPD therapy.


Subject(s)
Lung/diagnostic imaging , Pulmonary Disease, Chronic Obstructive/diagnostic imaging , Radiographic Image Enhancement/methods , Radiography, Thoracic/methods , Aged , Asthma/diagnosis , Asthma/drug therapy , Bronchodilator Agents/therapeutic use , Drug Combinations , Fluticasone/therapeutic use , Formoterol Fumarate/therapeutic use , Glycopyrrolate/analogs & derivatives , Glycopyrrolate/therapeutic use , Humans , Indans/therapeutic use , Lung/physiology , Male , Middle Aged , Pulmonary Disease, Chronic Obstructive/drug therapy , Quinolones/therapeutic use , Spirometry , Tiotropium Bromide/therapeutic use , Treatment Outcome
15.
Sci Rep ; 11(1): 16071, 2021 08 09.
Article in English | MEDLINE | ID: covidwho-1349689

ABSTRACT

To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.


Subject(s)
Algorithms , COVID-19/prevention & control , Deep Learning , Neural Networks, Computer , Pneumonia/diagnosis , COVID-19/complications , COVID-19/virology , Diagnosis, Differential , Humans , Pneumonia/complications , Radiography, Thoracic/methods , SARS-CoV-2/physiology , Sensitivity and Specificity , X-Rays
16.
Sci Rep ; 11(1): 16075, 2021 08 09.
Article in English | MEDLINE | ID: covidwho-1349687

ABSTRACT

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.


Subject(s)
Algorithms , COVID-19/diagnosis , Deep Learning , Radiography, Thoracic/methods , COVID-19/epidemiology , COVID-19/virology , Humans , Lung/diagnostic imaging , Lung/virology , Neural Networks, Computer , Pandemics , Reproducibility of Results , SARS-CoV-2/physiology , Sensitivity and Specificity , X-Rays
17.
Chest ; 160(1): e39-e44, 2021 07.
Article in English | MEDLINE | ID: covidwho-1291398

ABSTRACT

CASE PRESENTATION: A 65-year-old man presented with shortness of breath, gradually worsening for the previous 2 weeks, associated with dry cough, sore throat, and diarrhea. He denied fever, chills, chest pain, abdominal pain, nausea, or vomiting. He did not have any sick contacts or travel history outside of Michigan. His medical history included hypertension, diabetes mellitus, chronic kidney disease, morbid obesity, paroxysmal atrial fibrillation, and tobacco use. He was taking amiodarone, carvedilol, furosemide, pregabalin, and insulin. The patient appeared to be in mild respiratory distress. He was afebrile and had saturation at 93% on 3 L of oxygen, heart rate of 105 beats/min, BP of 145/99 mm Hg, and respiratory rate of 18 breaths/min. On auscultation, there were crackles on bilateral lung bases and chronic bilateral leg swelling with hyperpigmented changes. His WBC count was 6.0 K/cumm (3.5 to 10.6 K/cumm) with absolute lymphocyte count 0.7 K/cumm (1.0 to 3.8 K/cumm); serum creatinine was 2.81 mg/dL (0.7 to 1.3 mg/dL). He had elevated inflammatory markers (serum ferritin, C-reactive protein, lactate dehydrogenase, D-dimer, and creatinine phosphokinase). Chest radiography showed bilateral pulmonary opacities that were suggestive of multifocal pneumonia (Fig 1). Nasopharyngeal swab for SARS-CoV-2 was positive. Therapy was started with ceftriaxone, doxycycline, hydroxychloroquine, and methylprednisolone 1 mg/kg IV for 3 days. By day 3 of hospitalization, he required endotracheal intubation, vasopressor support, and continuous renal replacement. Blood cultures were negative; respiratory cultures revealed only normal oral flora, so antibiotic therapy was discontinued. On day 10, WBC count increased to 28 K/cumm, and chest radiography showed persistent bilateral opacities with left lower lobe consolidation. Repeat respiratory cultures grew Pseudomonas aeruginosa (Table 1). Antibiotic therapy with IV meropenem was started. His condition steadily improved; eventually by day 20, he was off vasopressors and was extubated. However, on day 23, he experienced significant hemoptysis that required reintubation and vasopressor support.


Subject(s)
Aspergillus niger/isolation & purification , COVID-19 , Hemoptysis , Invasive Pulmonary Aspergillosis , Pseudomonas aeruginosa/isolation & purification , SARS-CoV-2/isolation & purification , Superinfection , Voriconazole/administration & dosage , Aged , Antifungal Agents/administration & dosage , COVID-19/complications , COVID-19/diagnosis , COVID-19/physiopathology , COVID-19/therapy , Clinical Deterioration , Critical Illness/therapy , Critical Pathways , Diagnosis, Differential , Hemoptysis/diagnosis , Hemoptysis/etiology , Hemoptysis/therapy , Humans , Invasive Pulmonary Aspergillosis/complications , Invasive Pulmonary Aspergillosis/diagnosis , Invasive Pulmonary Aspergillosis/physiopathology , Lung/diagnostic imaging , Lung/physiopathology , Male , Radiography, Thoracic/methods , Respiration, Artificial/methods , Superinfection/diagnosis , Superinfection/microbiology , Superinfection/physiopathology , Superinfection/therapy , Tomography, X-Ray Computed/methods , Treatment Outcome
18.
J Healthc Eng ; 2021: 5513679, 2021.
Article in English | MEDLINE | ID: covidwho-1286755

ABSTRACT

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic/methods , Humans , Lung/diagnostic imaging , SARS-CoV-2 , Sensitivity and Specificity
19.
Scott Med J ; 66(3): 101-107, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1285149

ABSTRACT

OBJECTIVES: To devise a novel, simple chest x-ray (CXR) scoring system which would help in prognosticating the disease severity and ability to predict comorbidities and in-hospital mortality. METHODS: We included a total of 343 consecutive hospitalised patients with COVID-19 in this study. The chest x-rays of these patients were scored retrospectively by three radiologists independently. We divided CXR in to six zones (right upper, mid & lower and left, upper mid & lower zones). We scored each zone as- 0, 1 or 2 as follows- if that zone was clear (0) Ground glass opacity (1) or Consolidation (2). A total of score from 0 to 12 could be obtained. RESULTS: A CXR score cut off ≥3 independently predicted mortality. Along with a relatively higher NPV ≥80%, it reinforced the importance of CXR score is a screening tool to triage patients according to risk of mortality. CONCLUSIONS: We propose that Pennine score is a simple tool which can be adapted by various countries, experiencing a large surge in number of patients, to decide which patient would need a tertiary Hospital referral/admission as opposed to patients that can be managed locally or at basic/primary care hospitals.


Subject(s)
COVID-19/diagnostic imaging , Radiography, Thoracic , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/mortality , Comorbidity , Female , Hospital Mortality , Humans , Length of Stay , Male , Middle Aged , Predictive Value of Tests , Prognosis , Radiography, Thoracic/methods , Radiography, Thoracic/statistics & numerical data , Retrospective Studies , Risk Factors , Severity of Illness Index
20.
J Healthc Eng ; 2021: 6658058, 2021.
Article in English | MEDLINE | ID: covidwho-1277017

ABSTRACT

The COVID-19 pandemic has a significant negative effect on people's health, as well as on the world's economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers' attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.


Subject(s)
COVID-19/diagnosis , Deep Learning , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic/methods , SARS-CoV-2 , Algorithms , Diagnosis, Differential , Humans , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging
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