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1.
Technol Health Care ; 30(6): 1299-1314, 2022.
Статья в английский | MEDLINE | ID: covidwho-2154631

Реферат

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.


Тема - темы
COVID-19 , Humans , COVID-19/diagnostic imaging , Tomography, X-Ray Computed/methods , Machine Learning , Lung/diagnostic imaging , Algorithms , Retrospective Studies
2.
Front Public Health ; 10: 1046296, 2022.
Статья в английский | MEDLINE | ID: covidwho-2142366

Реферат

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.


Тема - темы
COVID-19 , Deep Learning , Humans , X-Rays , COVID-19/diagnostic imaging , Bayes Theorem , Neural Networks, Computer
3.
Radiology ; 305(3): 709-717, 2022 Dec.
Статья в английский | MEDLINE | ID: covidwho-2138184

Реферат

Background Post-COVID-19 condition encompasses symptoms following COVID-19 infection that linger at least 4 weeks after the end of active infection. Symptoms are wide ranging, but breathlessness is common. Purpose To determine if the previously described lung abnormalities seen on hyperpolarized (HP) pulmonary xenon 129 (129Xe) MRI scans in participants with post-COVID-19 condition who were hospitalized are also present in participants with post-COVID-19 condition who were not hospitalized. Materials and Methods In this prospective study, nonhospitalized participants with post-COVID-19 condition (NHLC) and posthospitalized participants with post-COVID-19 condition (PHC) were enrolled from June 2020 to August 2021. Participants underwent chest CT, HP 129Xe MRI, pulmonary function testing, and the 1-minute sit-to-stand test and completed breathlessness questionnaires. Control subjects underwent HP 129Xe MRI only. CT scans were analyzed for post-COVID-19 interstitial lung disease severity using a previously published scoring system and full-scale airway network (FAN) modeling. Analysis used group and pairwise comparisons between participants and control subjects and correlations between participant clinical and imaging data. Results A total of 11 NHLC participants (four men, seven women; mean age, 44 years ± 11 [SD]; 95% CI: 37, 50) and 12 PHC participants (10 men, two women; mean age, 58 years ±10; 95% CI: 52, 64) were included, with a significant difference in age between groups (P = .05). Mean time from infection was 287 days ± 79 (95% CI: 240, 334) and 143 days ± 72 (95% CI: 105, 190) in NHLC and PHC participants, respectively. NHLC and PHC participants had normal or near normal CT scans (mean, 0.3/25 ± 0.6 [95% CI: 0, 0.63] and 7/25 ± 5 [95% CI: 4, 10], respectively). Gas transfer (Dlco) was different between NHLC and PHC participants (mean Dlco, 76% ± 8 [95% CI: 73, 83] vs 86% ± 8 [95% CI: 80, 91], respectively; P = .04), but there was no evidence of other differences in lung function. Mean red blood cell-to-tissue plasma ratio was different between volunteers (mean, 0.45 ± 0.07; 95% CI: 0.43, 0.47]) and PHC participants (mean, 0.31 ± 0.10; 95% CI: 0.24, 0.37; P = .02) and between volunteers and NHLC participants (mean, 0.37 ± 0.10; 95% CI: 0.31, 0.44; P = .03) but not between NHLC and PHC participants (P = .26). FAN results did not correlate with Dlco) or HP 129Xe MRI results. Conclusion Nonhospitalized participants with post-COVID-19 condition (NHLC) and posthospitalized participants with post-COVID-19 condition (PHC) showed hyperpolarized pulmonary xenon 129 MRI and red blood cell-to-tissue plasma abnormalities, with NHLC participants demonstrating lower gas transfer than PHC participants despite having normal CT findings. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Parraga and Matheson in this issue.


Тема - темы
COVID-19 , Xenon Isotopes , Male , Humans , Female , Adult , Middle Aged , COVID-19/diagnostic imaging , Prospective Studies , Magnetic Resonance Imaging/methods , Lung/diagnostic imaging , Dyspnea
4.
Nucl Med Commun ; 43(12): 1163-1170, 2022 Dec 01.
Статья в английский | MEDLINE | ID: covidwho-2135755

Реферат

Cardiovascular diseases (CVDs) are the leading cause of mortality in Latin America and the Caribbean (LAC), with the risk in men being slightly higher than in women. The coronavirus disease 2019 (COVID-19) pandemic caused a significant reduction in the number of cardiac diagnostic procedures globally and in particular in LAC. Nuclear cardiology is available in the region, but there is variability in terms of existing technology, radiopharmaceuticals, and human resources. In the region, there are 2385 single photon emission computed tomography (SPECT) and 315 PET scanners, Argentina and Brazil have the largest number. There is an increasing number of new technologies such as cadmium-zinc-telluride (CZT) cardiac-dedicated gamma cameras, SPECT/computed tomography (CT), and PET/CT. All countries performed myocardial perfusion imaging studies, mainly gated-SPECT; the rest are multi-gated acquisition, mainly for cardiac toxicity; detection of viability; rest gated SPECT in patients with dilated cardiomyopathy, and bone-avid tracer cardiac scintigraphy for transthyretin cardiac amyloidosis diagnosis. Regarding other non-nuclear cardiac imaging modalities, Argentina, Colombia, and Chile have the highest ratio of CT scanners, while Brazil, Argentina, and Chile show the highest ratio of MRI scanners. The development of nuclear cardiology and other advanced imaging modalities is challenged by the high cost of equipment, lack of equipment maintenance and service, insufficient-specific training both for imaging specialists and referring clinicians, and lack of awareness of cardiologists or other referring physicians on the clinical applications of nuclear cardiology. Another important aspect to consider is the necessity of implementing cardiac imaging multimodality training. A joint work of nuclear medicine specialists, radiologists, cardiologists, and clinicians, in general, is mandatory to achieve this goal. National, regional, and international cooperation including support from scientific professional societies such as the American Society of Nuclear Cardiology and Latin American Association of Biology and Nuclear Medicine Societies, cardiological societies, and organizations such as the International Atomic Energy Agency, and Pan American Health Organization, as well as government commitment are key factors in the overall efforts to tackle the burden of cardiovascular diseases in the region.


Тема - темы
COVID-19 , Cardiology , Cardiovascular Diseases , Myocardial Perfusion Imaging , Male , Humans , Female , Latin America , Cardiovascular Diseases/diagnostic imaging , Positron Emission Tomography Computed Tomography , COVID-19/diagnostic imaging , Tomography, Emission-Computed, Single-Photon/methods , Caribbean Region
5.
Semin Respir Crit Care Med ; 43(6): 899-923, 2022 Dec.
Статья в английский | MEDLINE | ID: covidwho-2133781

Реферат

Radiology plays an important role in the management of the most seriously ill patients in the hospital. Over the years, continued advances in imaging technology have contributed to an improvement in patient care. However, even with such advances, the portable chest radiograph (CXR) remains one of the most commonly requested radiographic examinations. While they provide valuable information, CXRs remain relatively insensitive at revealing abnormalities and are often nonspecific. Chest computed tomography (CT) can display findings that are occult on CXR and is particularly useful at identifying and characterizing pleural effusions, detecting barotrauma including small pneumothoraces, distinguishing pneumonia from atelectasis, and revealing unsuspected or additional abnormalities which could result in increased morbidity and mortality if left untreated. CT pulmonary angiography is the modality of choice in the evaluation of pulmonary emboli which can complicate the hospital course of the ICU patient. This article will provide guidance for interpretation of CXR and thoracic CT images, discuss some of the invasive devices routinely used, and review the radiologic manifestations of common pathologic disease states encountered in ICU patients. In addition, imaging findings and complications of more specific clinical scenarios in which the incidence has increased in the ICU setting, such as patients who are immunocompromised, have interstitial lung disease, or COVID-19, will also be discussed. Communication between the radiologist and intensivist, particularly on complicated cases, is important to help increase diagnostic accuracy and leads to an improvement in the management of the most critically ill patients.


Тема - темы
COVID-19 , Pneumothorax , Humans , COVID-19/diagnostic imaging , Intensive Care Units , Tomography, X-Ray Computed , Communication
6.
Acute Med ; 21(3): 131-138, 2022.
Статья в английский | MEDLINE | ID: covidwho-2146878

Реферат

BACKGROUND: Coronavirus disease 2019 has had a dramatic impact on the delivery of acute care globally. Accurate risk stratification is fundamental to the efficient organisation of care. Point-of-care lung ultrasound offers practical advantages over conventional imaging with potential to improve the operational performance of acute care pathways during periods of high demand. The Society for Acute Medicine and the Intensive Care Society undertook a collaborative evaluation of point-of-care imaging in the UK to describe the scope of current practice and explore performance during real-world application. METHODS: A retrospective service evaluation was undertaken of the use of point-of-care lung ultrasound during the initial wave of coronavirus infection in the UK. We report an evaluation of all imaging studies performed outside the intensive care unit. An ordinal scale was used to measure the severity of loss of lung aeration. The relationship between lung ultrasound, polymerase chain reaction for SARS-CoV-2 and 30-day outcomes were described using logistic regression models. RESULTS: Data were collected from 7 hospitals between February and September 2020. In total, 297 ultrasound examinations from 295 patients were recorded. Nasopharyngeal swab samples were positive in 145 patients (49.2% 95%CI 43.5-54.8). A multivariate model combining three ultrasound variables showed reasonable discrimination in relation to the polymerase chain reaction reference (AUC 0.77 95%CI 0.71-0.82). The composite outcome of death or intensive care admission at 30 days occurred in 83 (28.1%, 95%CI 23.3-33.5). Lung ultrasound was able to discriminate the composite outcome with a reasonable level of accuracy (AUC 0.76 95%CI 0.69-0.83) in univariate analysis. The relationship remained statistically significant in a multivariate model controlled for age, sex and the time interval from admission to scan Conclusion: Point-of-care lung ultrasound is able to discriminate patients at increased risk of deterioration allowing more informed clinical decision making.


Тема - темы
COVID-19 , Humans , COVID-19/diagnostic imaging , Point-of-Care Systems , Retrospective Studies , SARS-CoV-2 , Lung/diagnostic imaging , United Kingdom/epidemiology
7.
Am J Med Sci ; 361(5): 646-649, 2021 05.
Статья в английский | MEDLINE | ID: covidwho-2129825

Реферат

The SARS-CoV-2 virus, or COVID-19, is responsible for the current global pandemic and has resulted in the death of over 400,000 in the United States. Rates of venous thromboembolism have been noted to be much higher in those infected with COVID-19. Here we report a case-series of COVID-19 patients with diverse presentations of pulmonary embolism (PE). We also briefly describe the pathophysiology and mechanisms for pulmonary embolism in COVID-19. These cases indicate a need to maintain a high index of suspicion for PE in patients with COVID-19, as well as the need to consider occult COVID-19 infection in patients with PE in the right clinical circumstance.


Тема - темы
COVID-19 , Pulmonary Embolism , SARS-CoV-2 , Acute Disease , Adult , COVID-19/complications , COVID-19/diagnostic imaging , COVID-19/epidemiology , COVID-19/physiopathology , Humans , Male , Middle Aged , Pulmonary Embolism/diagnostic imaging , Pulmonary Embolism/epidemiology , Pulmonary Embolism/etiology , Pulmonary Embolism/physiopathology , United States/epidemiology
8.
JMIR Public Health Surveill ; 7(9): e28005, 2021 09 21.
Статья в английский | MEDLINE | ID: covidwho-2141326

Реферат

BACKGROUND: The clinical, laboratory, and imaging features of COVID-19 disease are variable. Multiple factors can affect the disease progression and outcome. OBJECTIVE: This study aimed to analyze the clinical, laboratory, and imaging features of COVID-19 in Jordan. METHODS: Clinical, laboratory, and imaging data were collected for 557 confirmed COVID-19 patients admitted to Prince Hamzah Hospital (PHH), Jordan. Analysis was performed using appropriate statistical tests with SPSS version 24. RESULTS: Of the 557 COVID-19 polymerase chain reaction (PCR)-positive cases admitted to PHH, the mean age was 34.4 years (SD 18.95 years; range 5 weeks to 87 years), 86.0% (479/557) were male, 41% (29/70) were blood group A+, and 57.1% (93/163) were overweight or obese. Significant past medical history was documented in 25.9% (144/557), significant surgical history in 12.6% (70/557), current smoking in 14.9% (83/557), and pregnancy in 0.5% (3/557). The mean duration of hospitalization was 16.4 (SD 9.3; range 5 to 70) days; 52.6% (293/557) were asymptomatic, and 12.9% (72/557) had more than 5 symptoms, with generalized malaise and dry cough the most common symptoms. Only 2.5% (14/557) had a respiratory rate over 25 breaths/minute, and 1.8% (10/557) had an oxygen saturation below 85%. Laboratory investigations showed a wide range of abnormalities, with lymphocytosis and elevated C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and D-dimer the most common abnormalities. Ground glass opacity was the most common imaging finding. Men had a significantly higher frequency of symptoms, incidence of smoking, reduced hemoglobin, increased monocyte %, elevated creatinine levels, and intensive care unit admissions compared with women (P<.05). Hospitalization duration was associated with increased age, male gender, symptom score, history of smoking, elevated systolic blood pressure, elevated respiratory rate, and elevated monocyte %, CRP, ESR, creatinine, and D-dimer (P<.05). CONCLUSIONS: Most COVID-19 cases admitted to PHH were asymptomatic. Variabilities in symptoms, signs, laboratory results, and imaging findings should be noted. Increased age, male gender, smoking history, and elevated inflammatory markers were significantly associated with longer duration of hospitalization.


Тема - темы
COVID-19/diagnosis , COVID-19/therapy , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnostic imaging , COVID-19/epidemiology , Child , Child, Preschool , Cohort Studies , Cross-Sectional Studies , Female , Hospitalization/statistics & numerical data , Humans , Infant , Jordan/epidemiology , Laboratories , Male , Middle Aged , Pregnancy , Young Adult
9.
Front Public Health ; 10: 931480, 2022.
Статья в английский | MEDLINE | ID: covidwho-2123468

Реферат

Background: Omicron has become the dominant variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally. We aimed to compare the clinical and pulmonary computed tomography (CT) characteristics of the patients infected with SARS-CoV-2 Omicron with those of patients infected with the Alpha viral strain. Methods: Clinical profiles and pulmonary CT images of 420 patients diagnosed with coronavirus disease-2019 (COVID-19) at Ningbo First Hospital between January 2020 and April 2022 were collected. Demographic characteristics, symptoms, and imaging manifestations of patients infected with the SARS-CoV-2 Omicron variant were compared with those of patients infected with the Alpha strain. Results: A total of 38 patients were diagnosed to be infected with the Alpha strain of SARS-CoV-2, whereas 382 patients were thought to be infected with the Omicron variant. Compared with patients infected with the Alpha strain, those infected with the Omicron variant were younger, and a higher proportion of men were infected (P < 0.001). Notably, 93 (24.3%) of the patients infected with Omicron were asymptomatic, whereas only two (5.3%) of the patients infected with the Alpha strain were asymptomatic. Fever (65.8%), cough (63.2%), shortness of breath (21.1%), and diarrhea (21.1%) were more common in patients infected with the SARS-CoV-2 Alpha strain, while runny nose (24.1%), sore throat (31.9%), body aches (13.6%), and headache (12.3%) were more common in patients with the Omicron variant. Compared with 33 (86.84%) of 38 patients infected with the Alpha strain, who had viral pneumonia on pulmonary CT images, only 5 (1.3%) of 382 patients infected with the Omicron variant had mild foci. In addition, the distribution of opacities in the five patients was unilateral and centrilobular, whereas most patients infected with the Alpha strain had bilateral involvement and multiple lesions in the peripheral zones of the lung. Conclusion: The SARS-CoV-2 Alpha strain mainly affects the lungs, while Omicron is confined to the upper respiratory tract in patients with COVID-19.


Тема - темы
COVID-19 , SARS-CoV-2 , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Tomography, X-Ray Computed
10.
PLoS One ; 17(11): e0276250, 2022.
Статья в английский | MEDLINE | ID: covidwho-2119372

Реферат

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.


Тема - темы
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Artificial Intelligence , X-Rays , Semantics
11.
PLoS One ; 17(11): e0276859, 2022.
Статья в английский | MEDLINE | ID: covidwho-2119142

Реферат

OBJECTIVES: A convenient way to conduct pulmonary function tests while preventing infectious diseases was proposed, together with countermeasures for severe coronavirus disease 2019 (COVID-19). The correlation between diagnosis result and diagnosis result was examined for patients with mild chronic obstructive pulmonary disease (COPD) of the most abounding as a subject of spirometry, and the possibility of using this method as an alternative to spirometry was examined. SETTING: This study was conducted in Kanagawa, Japan. PARTICIPANTS: Ten normal volunteers and 15 volunteers with mild COPD participated in this study. OUTCOME MEASURES: All images were taken by EXAVISTA (Hitachi, Japan) between October 2019 and February 2020. Continuous fluoroscopic images were taken in 12.5 frames per second for 10-20 s per subject. Images that do not adopt the automatic image processing of the equipment and only carry out the signal correction of each pixel were used for the analysis. RESULTS: The mean total dose for all volunteers was 0.2 mGy. There was no major discrepancy in the detection of lung field geometry, and no diagnostic problems were noted by the radiologist and physician. CONCLUSIONS: Existing X-ray cine imaging was used to extract frequency-tunable imaging. It is possible to identify abnormal regions on the images compared to spirometry, and it does not require maximum effort respiration; therefore, it is possible to perform a stable examination because the patient's physical condition and the ability of laboratory technicians on the day are less affected. This can also be used as a countermeasure in examining patients with infectious diseases. TRIAL REGISTRATION: UMIN UMIN000043868.


Тема - темы
COVID-19 , Pulmonary Disease, Chronic Obstructive , Respiration Disorders , Humans , X-Rays , COVID-19/diagnostic imaging , Lung/diagnostic imaging
12.
Technol Health Care ; 30(6): 1273-1286, 2022.
Статья в английский | MEDLINE | ID: covidwho-2119015

Реферат

BACKGROUND: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. OBJECTIVE: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. METHODS: The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. RESULT: The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. CONCLUSION: The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.


Тема - темы
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , X-Rays , Tomography, X-Ray Computed/methods
13.
Curr Med Imaging ; 18(14): 1536-1539, 2022.
Статья в английский | MEDLINE | ID: covidwho-2117596

Реферат

BACKGROUND: Coronavirus disease 2019 (COVID-19, previously known as novel coronavirus [2019-nCoV]), first reported in China, has now been declared a global health emergency by World Health Organization. The clinical severity ranges from asymptomatic individuals to death. Here, we report clinical features and radiological changes of a cured family cluster infected with COVID-19. CASE PRESENTATION: In this report, we enrolled a family of 4 members who were admitted to our hospital in January 2020. We performed a detailed analysis of each patient's records. All patients underwent chest computed tomography (CT) examination with 120 kilovolts peak and 150 kilovolt-ampere. Realtime polymerase chain reaction (RT-PCR) examinations for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid were done using nasopharyngeal swabs. CONCLUSION: In the family members infected with COVID-19 who were accompanied by other diseases or had low immunity, the pneumonia was prone to be aggravated.


Тема - темы
COVID-19 , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed , Radiography , China
14.
Comput Methods Programs Biomed ; 226: 107109, 2022 Nov.
Статья в английский | MEDLINE | ID: covidwho-2117158

Реферат

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.


Тема - темы
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , X-Rays , SARS-CoV-2 , Speech , Cough/diagnostic imaging , Early Diagnosis
15.
Sci Rep ; 12(1): 19186, 2022 Nov 10.
Статья в английский | MEDLINE | ID: covidwho-2116888

Реферат

Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identify the virus from bodily fluids and since the virus causes pneumonia, which results in lung inflammation, the presence of the virus can also be detected using medical imaging by expert radiologists. The success of each diagnostic method is measured by the hit rate for identifying Covid infections. However, the access for people to each diagnosis tool can be limited, depending on the geographic region and, since Covid treatment denotes a race against time, the diagnosis duration plays an important role. Hospitals with X-ray opportunities are widely distributed all over the world, so a method investigating lung X-ray images for possible Covid-19 infections would offer itself. Promising results have been achieved in the literature in automatically detecting the virus using medical images like CT scans and X-rays using supervised artificial neural network algorithms. One of the major drawbacks of supervised learning models is that they require enormous amounts of data to train, and generalize on new data. In this study, we develop a Swish activated, Instance and Batch normalized Residual U-Net GAN with dense blocks and skip connections to create synthetic and augmented data for training. The proposed GAN architecture, due to the presence of instance normalization and swish activation, can deal with the randomness of luminosity, that arises due to different sources of X-ray images better than the classical architecture and generate realistic-looking synthetic data. Also, the radiology equipment is not generally computationally efficient. They cannot efficiently run state-of-the-art deep neural networks such as DenseNet and ResNet effectively. Hence, we propose a novel CNN architecture that is 40% lighter and more accurate than state-of-the-art CNN networks. Multi-class classification of the three classes of chest X-rays (CXR), ie Covid-19, healthy and Pneumonia, is performed using the proposed model which had an extremely high test accuracy of 99.2% which has not been achieved in any previous studies in the literature. Based on the mentioned criteria for developing Corona infection diagnosis, in the present study, an Artificial Intelligence based method is proposed, resulting in a rapid diagnostic tool for Covid infections based on generative adversarial and convolutional neural networks. The benefit will be a high accuracy of lung infection identification with 99% accuracy. This could lead to a support tool that helps in rapid diagnosis, and an accessible Covid identification method using CXR images.


Тема - темы
COVID-19 , Deep Learning , Pneumonia , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Artificial Intelligence
16.
Sensors (Basel) ; 22(22)2022 Nov 21.
Статья в английский | MEDLINE | ID: covidwho-2116085

Реферат

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.


Тема - темы
Brain Neoplasms , COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , Diagnosis, Computer-Assisted , Computers
17.
Medicine (Baltimore) ; 101(39): e30744, 2022 Sep 30.
Статья в английский | MEDLINE | ID: covidwho-2113766

Реферат

OBJECTIVE: The aim of this study was to compare the radiographic features of patients with progressive and nonprogressive coronavirus disease 2019 (COVID-19) pneumonia. METHODS: PubMed, Embase, and Cochrane Library databases were searched from January 1, 2020, to February 28, 2022, by using the keywords: "COVID-19", "novel Coronavirus", "2019-novel coronavirus", "CT", "radiology" and "imaging". We summarized the computed tomography manifestations of progressive and nonprogressive COVID-19 pneumonia. The meta-analysis was performed using the Stata statistical software version 16.0. RESULTS: A total of 10 studies with 1092 patients were included in this analysis. The findings of this meta-analysis indicated that the dominating computed tomography characteristics of progressive patients were a crazy-paving pattern (odds ratio [OR] = 2.10) and patchy shadowing (OR = 1.64). The dominating lesions distribution of progressive patients were bilateral (OR = 11.62), central mixed subpleural (OR = 1.37), and central (OR = 1.36). The other dominating lesions of progressive patients were pleura thickening (OR = 2.13), lymphadenopathy (OR = 1.74), vascular enlargement (OR = 1.39), air bronchogram (OR = 1.29), and pleural effusion (OR = 1.29). Two patterns of lesions showed significant links with the progression of disease: nodule (P = .001) and crazy-paving pattern (P = .023). Four lesions distribution showed significant links with the progression of disease: bilateral (P = .004), right upper lobe (P = .003), right middle lobe (P = .001), and left upper lobe (P = .018). CONCLUSION: Nodules, crazy-paving pattern, and/or new lesions in bilateral, upper and middle lobe of right lung, and lower lobe of left lung may indicate disease deterioration. Clinicians should formulate or modify treatment strategies in time according to these specific conditions.


Тема - темы
COVID-19 , Pneumonia , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/pathology , Pneumonia/pathology , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
18.
Sensors (Basel) ; 22(21)2022 Nov 07.
Статья в английский | MEDLINE | ID: covidwho-2110217

Реферат

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.


Тема - темы
COVID-19 , Pneumonia, Viral , Humans , COVID-19/diagnostic imaging , Pandemics , COVID-19 Testing , X-Rays
19.
Medicine (Baltimore) ; 101(37): e30655, 2022 Sep 16.
Статья в английский | MEDLINE | ID: covidwho-2107668

Реферат

The spread of abnormal opacity on chest computed tomography (CT) has been reported as a predictor of coronavirus disease 2019 (COVID-19) severity; however, the relationship between CT findings and prognosis in patients with severe COVID-19 remains unclear. The objective of this study was to evaluate the extent of abnormal opacity on chest CT and its association with prognosis in patients with COVID-19 in a critical care medical center, using a simple semi-quantitative method. This single-center case-control study included patients diagnosed with severe COVID-19 pneumonia who were admitted to a critical care center. The diagnosis of COVID-19 was based on positive results of a reverse transcription polymerase chain reaction test. All patients underwent non-contrast whole-body CT upon admission. Six representative axial chest CT images were selected for each patient to evaluate the extent of lung lesions. The percentage of the area involved in the representative CT images was visually assessed by 2 radiologists and scored on 4-point scale to obtain the bedside CT score, which was compared between patients who survived and those who died using the Mann-Whitney U test. A total of 63 patients were included in this study: 51 survived and 12 died after intensive treatment. The inter-rater reliability of bedside scores between the 2 radiologists was acceptable. The median bedside CT score of the survival group was 12.5 and that of the mortality group was 16.5; the difference between the 2 groups was statistically significant. The degree of opacity can be easily scored using representative CT images in patients with severe COVID-19 pneumonia, without sophisticated software. A greater extent of abnormal opacity is associated with poorer prognosis. Predicting the prognosis of patients with severe COVID-19 could facilitate prompt and appropriate treatment.


Тема - темы
COVID-19 , Pneumonia , COVID-19/diagnostic imaging , Case-Control Studies , Critical Care , Humans , Reproducibility of Results , Tomography, X-Ray Computed/methods
20.
Turk J Med Sci ; 52(5): 1506-1512, 2022 Oct.
Статья в английский | MEDLINE | ID: covidwho-2101126

Реферат

BACKGROUND: The coronavirus disease 2019 (COVID-19) mostly manifests with fever, shortness of breath, and cough, has also been found to cause some neurological symptoms, such as anosmia and ageusia. The aim of the study was to present the magnetic resonance imaging (MRI) findings of patients with anosmia-hyposmia symptoms and to discuss potential mechanisms in light of these findings. METHODS: Of the 2412 patients diagnosed with COVID-19-related pneumonia (RT-PCR at least once + clinically confirmed) between March and December 2020, 15 patients underwent olfactory MRI to investigate the cause of ongoing anosmia/ hyposmia symptoms were included in the study. RESULTS: Eleven (73.3%) patients were female and four (26.7%) were male. A total of eight patients (53.3%) showed thickening in the olfactory cleft region, where the olfactory epithelium is located. In nine patients (60%), enhancement was observed in the olfactory cleft region. Diffusion-weighted imaging showed restricted diffusion in three patients (20%) (corpus callosum splenium in one patient, thalamus mediodorsal nucleus in one patient, and mesencephalon in one patient). DISCUSSION: This study revealed that there is a relationship between anosmia and MRI findings. Larger studies can enlighten the pathophysiological mechanism and shed light on both diagnosis and new treatments.


Тема - темы
COVID-19 , Olfaction Disorders , Humans , Male , Female , Anosmia/diagnostic imaging , Anosmia/etiology , COVID-19/complications , COVID-19/diagnostic imaging , Olfaction Disorders/diagnostic imaging , Olfaction Disorders/etiology , Magnetic Resonance Imaging , Corpus Callosum/pathology
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