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1.
10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022 ; 308:3-14, 2022.
Article in English | Scopus | ID: covidwho-1971636

ABSTRACT

Due to the rapid spread of the COVID-19 respiratory pathology, an effective diagnosis of positive cases is necessary to stop the contamination. CT scans offer a 3D view of the patient’s thorax and COVID-19 appears as ground glass opacities on these images. This paper describes a deep learning based approach to automatically classify CT scan images as COVID-19 or not COVID-19. We first build a dataset and preprocess this data. Preprocessing includes normalization, resizing and data augmentation. Then, the training step is based on a neural network used for tuberculosis pathology. Training of the dataset is performed using a 3D convolutional neural network. The results of the neural network model on the test set returns an accuracy of 80%. A prototype of the approach is implemented in a form of a web application to assist doctors and speed up the COVID-19 diagnosis. Codes of both the training and the web application are available online for further research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

2.
International Conference on Computing and Communication Networks, ICCCN 2021 ; 394:261-275, 2022.
Article in English | Scopus | ID: covidwho-1971595

ABSTRACT

During the outbreak of the COVID-19 pandemic, it is important to improve early diagnosis using effective ways in order to lower the risks and further spread of the viruses as early as possible. This is also important when it comes to appropriate treatments and the reduction of mortality rates. In this respect, computer tomography (CT) scanning is a useful technique in detecting COVID-19. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19 positives and 86 COVID-19 negative patients, all from Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies demonstrate that this dataset is effectively utilized deep learning-based models for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a pre-processing stage. Then, the performance of the proposed method is evaluated using InceptionV3 and Xception convolutional neural networks, yielding a 96.20% and 96.55% accuracy rate and 95.00% and 95.50% F1-score, respectively. These state-of-the-art models are observed to detect COVID-19 cases faster and more accurately. In addition, the fine-tuning stage of the convolutional neural network (CNN) features sufficiently improves this accuracy rate. For these features, the support vector machine (SVM) classifier is used, resulting in remarkable 96.76% accuracy rate and 95.81% F1-score. The implications of the proposed method are immense both for present-day applications as well as future developments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
Diagnostics (Basel) ; 12(6)2022 Jun 20.
Article in English | MEDLINE | ID: covidwho-1969124

ABSTRACT

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

4.
CLINICAL AND EXPERIMENTAL HEALTH SCIENCES ; 12(2):528-532, 2022.
Article in English | Web of Science | ID: covidwho-1970039

ABSTRACT

Objective: COVID-19 pandemic, causing approximately 3 million deaths over worldwide, still continues. Effect of COVID-19 pneumonia after treatment on the lungs still not know. Although widely using computed tomography (CT) for diagnosing COVID-19 pneumonia, there is not enough study to determine damage of lung after treatment in COVID-19 pneumonia. In this study, our aim was to evaluate lung parenchyma changes in COVID-19 pneumonia after treatment with volumetric study, quantitatively. Methods: 25 patients, who has CT at the time of diagnosis (CT1) and after 28 +/- 2 days (CT2), and positive polymerase chain reaction test, were included in this retrospective single center study. Total lung voliime (TLV) and emphysematous lung (ELV) volume of CT1 and CT2 were calculated automatically by using Myrian (R) XP-Lung and Percentage of emphysematous area (PEA) was calculated by dividing ELV by TLV. Differences between CT1 and CT2 in PEA and in TLV and ELV was determined by Wilcoxon and Paired sample t test, respectively. Results: Although higher TLV was found in CT2 (4216,43 +/- 1048,99 cm3) than CT1 (3943,22 +/- 1177,16 cm3), there was no statistical significance difference (p=0.052) between CT1 and CT2. ELV was statistically (p=0.017) higher in CT2 (937,22 +/- 486,89 cm3) than CT1 (716,26 +/- 471,65 cm3). There was a strong indication that the medians were significantly different in PEA (p=0,009). Conclusion: Our study showed that there were emphysematous changes in lung parenchyma after COVID-19 pneumonia with CT, quantitatively and in our knowledge, this is the first study that evaluating lung changes quantitative after COVID-19 pneumonia.

5.
Quantitative Imaging in Medicine and Surgery ; 0(0):0-0, 2022.
Article in English | Web of Science | ID: covidwho-1969928

ABSTRACT

Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals;all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/ severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880;P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19,which may facilitate individualized management of patients with this disease.

6.
Mol Imaging Radionucl Ther ; 31(2): 169-171, 2022 Jun 27.
Article in English | MEDLINE | ID: covidwho-1969628

ABSTRACT

A 50-year-old female patient underwent (18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) following modified radical mastectomy for cancer of the left breast. Ten days before the PET/CT, the coronavirus disease-2019 (COVID-19) vaccine was injected intramuscularly into the right deltoid muscle. Increased (18F-FDG uptake of maximum standardized uptake value (11.0) was observed in the lymph nodes of the right axilla, which had not been observed in the previous PET/CT. The size of the oval-shaped lymph nodes was up to approximately 11×9 mm; however, it was larger than that observed on the previous PET/CT. We contemplate that the increased (18F-FDG uptake was a reactive change in the lymph nodes associated with the COVID-19 vaccine.

7.
Radiol Case Rep ; 17(10): 3659-3662, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1967028

ABSTRACT

Myositis and myonecrosis are rare sequela of coronavirus disease 2019 (COVID-19). Until now, it has not been seen in muscles of the head and neck. We present a 22-year-old male with 4 months of retroauricular headaches following COVID-19 infection. Magnetic resonance imaging revealed rim-enhancing fluid collections in the bilateral masticator spaces which were sampled by fine-needle aspiration. We also discuss this case in the context of the current understanding of COVID-19-related myositis.

8.
Radiol Case Rep ; 17(10): 3713-3717, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1967027

ABSTRACT

Cerebral venous sinus thrombosis (CVST) is a rather uncommon disorder. CVST is potentially lethal, therefore early detection and treatment is critical. CVST has been linked to pregnancy and puerperium, while COVID-19 infection has been linked to a hypercoagulable state. CVST can be difficult to detect and treat due to the wide range of neurological manifestations, especially in patients with hypercoagulability. The goal of this study is to conduct a literature review and present a unique case of a pregnant woman with CVST who had left hemiplegia and headache. After 6 months of treatment in the hospital, the patient's hemiplegia was fully resolved. Here, we discuss the treatment of CVST in pregnant women who have a suspected past COVID-19 infection.

9.
Lancet Reg Health Eur ; 21: 100462, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1966923

ABSTRACT

Background: The Omicron wave of COVID-19 in England peaked in January 2022 resulting from the rapid transmission of the Omicron BA.1 variant. We investigate the spread and dynamics of the SARS-CoV-2 epidemic in the population of England during February 2022, by region, age and main SARS-CoV-2 sub-lineage. Methods: In the REal-time Assessment of Community Transmission-1 (REACT-1) study we obtained data from a random sample of 94,950 participants with valid throat and nose swab results by RT-PCR during round 18 (8 February to 1 March 2022). Findings: We estimated a weighted mean SARS-CoV-2 prevalence of 2.88% (95% credible interval [CrI] 2.76-3.00), with a within-round effective reproduction number (R) overall of 0.94 (0·91-0.96). While within-round weighted prevalence fell among children (aged 5 to 17 years) and adults aged 18 to 54 years, we observed a level or increasing weighted prevalence among those aged 55 years and older with an R of 1.04 (1.00-1.09). Among 1,616 positive samples with sublineages determined, one (0.1% [0.0-0.3]) corresponded to XE BA.1/BA.2 recombinant and the remainder were Omicron: N=1047, 64.8% (62.4-67.2) were BA.1; N=568, 35.2% (32.8-37.6) were BA.2. We estimated an R additive advantage for BA.2 (vs BA.1) of 0.38 (0.34-0.41). The highest proportion of BA.2 among positives was found in London. Interpretation: In February 2022, infection prevalence in England remained high with level or increasing rates of infection in older people and an uptick in hospitalisations. Ongoing surveillance of both survey and hospitalisations data is required. Funding: Department of Health and Social Care, England.

10.
Signal Process Image Commun ; 108: 116835, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1966640

ABSTRACT

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

11.
Comput Methods Programs Biomed ; 225: 107053, 2022 Jul 31.
Article in English | MEDLINE | ID: covidwho-1966447

ABSTRACT

OBJECTIVE: Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival. METHODS: The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques. RESULTS: The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness. CONCLUSION: With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.

12.
Clin Imaging ; 90: 78-89, 2022 Jul 29.
Article in English | MEDLINE | ID: covidwho-1966437

ABSTRACT

Cardiovascular involvement is a common complication of COVID-19 infection and is associated to increased risk of unfavorable outcome. Advanced imaging modalities (coronary CT angiography and Cardiac Magnetic Resonance) play a crucial role in the diagnosis, follow-up and risk stratification of patients affected by COVID-19 pneumonia with suspected cardiovascular involvement. In the present manuscript we firstly review current knowledge on the mechanisms by which SARS-CoV-2 can trigger endothelial and myocardial damage. Secondly, the implications of the cardiovascular damage on patient's prognosis are presented. Finally, we provide an overview of the main findings at advanced cardiac imaging characterizing COVID-19 in the acute setting, in the post-acute syndrome, and after vaccination, emphasizing the potentiality of CT and CMR, the indication and their clinical implications.

13.
Chest ; 2022 Jul 31.
Article in English | MEDLINE | ID: covidwho-1966429

ABSTRACT

BACKGROUND: Lung ultrasound (LUS) is useful to diagnose and assess the severity of pulmonary lesions during CoViD-19 related acute respiratory distress syndrome (CoARDS). A conventional LUS score is proposed to measure the loss of aeration during CoARDS. However, this score was validated during the pre-COVID-19 era in ARDS patients in intensive care and does not consider the differences with CoARDS. An alternative LUS method is based on grading the percentage of extension of the typical signs of COVID-19 pneumonia on the lung surface (LUSext). RESEARCH QUESTION: Is LUSext feasible in COVID-19 patients at onset of disease and does it correlate with the volumetric measure of severity of COVID-19 pneumonia lesions at computed tomography (CTvol)? STUDY DESIGN AND METHOD: This observational study enrolled a convenience sampling of patients in the emergency department with confirmed COVID-19 and demonstration of pneumonia at bedside LUS and CT scan. LUSext was visually quantified. All CT studies were retrospectively analyzed by a specifically designed software to calculate the CTvol. The correlation between LUSext and CTvol, and the correlations of each score with PaO2/FiO2 ratio (P/F) were calculated. RESULTS: We analyzed data from 179 patients. Feasibility of LUSext was 100%. Time to perform LUS scan was 5 + 1.5 mins. LUSext and CTvol were positively correlated, with R = 0.67, P < 0.0001. Both LUSext and CTvol showed negative correlation with P/F, respectively with R = -0.66 and R = -0.54; P <0.0001. INTERPRETATION: LUSext is a valid measure of the severity of the lesions when compared to CT scan. Not only are LUSext and CTvol correlated, but they also have similar inverse correlation with the severity of respiratory failure. LUSext is a practical and simple bedside measure of the severity of pneumonia in CoARDS, whose clinical and prognostic impact need to be further investigated.

14.
Appl Soft Comput ; 128: 109401, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1966354

ABSTRACT

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

15.
Ann Med Surg (Lond) ; 80: 104242, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1966305

ABSTRACT

Introduction: and Importance: Maxillary actinomycosis is a persistent, very rare disease produced by Actinomyces species which may include only soft tissue or bone or the two together. Actinomycotic osteomyelitis of maxilla is very infrequent when compared to mandible. Case presentation: Here we are conferring a case of an elderly male patient who had history of COVID-19 infection 4 months ago, with constant complaint of non-remitting vague pain in the region of maxilla with tooth loosening and extractions. He was given a provisional diagnosis of chronic osteomyelitis of maxilla which was later on proved by histopathology as actinomycotic osteomyelitis. Clinical discussion: A saprophytic fungus causes mucor mycosis, and it is quite unusual. Strawberry gingivitis is one of the signs and symptoms. Mucormycosis and post-covid oral maxillofacial problems can be improved with early diagnosis. Oral Mucormycosis should be suspected in individuals with weakened immune systems, uncontrolled diabetes or post-covid instances. Surgery and adequate antibiotic treatment are necessary to treat actinomycosis. Infection may return after a period of inactivity, so long-term follow-up is necessary. Conclusion: We conclude a positive causal association between COVID-19 and actinomycosis. Maxillary osteomyelitis, a very rare infection, and in our case, the causative organism was Actinomyces Patients who have been infected should be tested for Actinomycin, which may masquerade as a head and neck illness.

16.
Med Phys ; 2022 Aug 02.
Article in English | MEDLINE | ID: covidwho-1966080

ABSTRACT

PURPOSE: Corona virus disease 2019 (COVID-19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID-19-infected regions. Accurate segmentation infection area of COVID-19 can contribute screen confirmed cases. METHODS: We designed a segmentation network for COVID-19-infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low-level feature f2 were extracted by the edge attention module. Second, we carefully designed the structure of the attention position module (APM) to extract high-level feature f5 and detect infected regions. Finally, we proposed a context exploration module consisting of two parallel explore blocks, which can remove some false positives and false negatives to reach more accurate segmentation results. RESULTS: Experimental results show that, on the public COVID-19 dataset, the Dice, sensitivity, specificity, S α ${S}_\alpha $ , E ∅ m e a n $E_\emptyset ^{mean}$ , and mean absolute error (MAE) of our method are 0.755, 0.751, 0.959, 0.795, 0.919, and 0.060, respectively. Compared with the latest COVID-19 segmentation model Inf-Net, the Dice similarity coefficient of our model has increased by 7.3%; the sensitivity (Sen) has increased by 5.9%. On contrary, the MAE has dropped by 2.2%. CONCLUSIONS: Our method performs well on COVID-19 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID-19 effectively and save the labor power of clinicians and radiologists.

17.
Concurr Comput ; : e7211, 2022 Jul 30.
Article in English | MEDLINE | ID: covidwho-1966037

ABSTRACT

A novel corona virus (COVID-19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID-19. To predict the patients of COVID-19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya-tunicate swarm algorithm driven generative adversarial network (Jaya-TSA with GAN) is proposed in this research to find patients of COVID-19 infections. The developed Jaya-TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID-19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya-TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya-TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

18.
Tomography ; 8(3): 1578-1585, 2022 06 17.
Article in English | MEDLINE | ID: covidwho-1964057

ABSTRACT

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.


Subject(s)
COVID-19 , Aged , COVID-19/diagnostic imaging , Female , Humans , Infant , Lung/diagnostic imaging , Middle Aged , Pandemics , Prospective Studies , Tomography, X-Ray Computed/methods
19.
International Journal of Advanced Technology and Engineering Exploration ; 9(90):623-643, 2022.
Article in English | ProQuest Central | ID: covidwho-1964885

ABSTRACT

A rapid diagnostic system is a primary role in the healthcare system exclusively during a pandemic situation to control contagious diseases like coronavirus disease-2019 (COVID-19). Many countries remain lacking to spot COVID cases by the reverse transcription-polymerase chain reaction (RT-PCR) test. On this stretch, deep learning algorithms have been strengthened the medical image processing system to analyze the infection, categorization, and further diagnosis. It is motivated to discover the alternate way to identify the disease using existing medical implications. Hence, this review narrated the character and attainment of deep learning algorithms at each juncture from origin to COVID-19. This literature highlights the importance of deep learning and further focused the medical image processing research on handling the data of magnetic resonance imaging (MRI), computed tomography (CT) scan, and electromagnetic radiation (X-ray) images. Additionally, this systematic review tabulates the popular deep learning networks with operational parameters, peer-reviewed research with their outcomes, popular nets, and prevalent datasets, and highlighted the facts to stimulate future research. The consequence of this literature ascertains convolutional neural network-based deep learning approaches work better in the medical image processing system, and especially it is very supportive of sorting out the COVID-19 complications.

20.
Current Respiratory Medicine Reviews ; 18(2):152-157, 2022.
Article in English | Scopus | ID: covidwho-1963208

ABSTRACT

Background: The present study aimed to assess the prevalence of persistent/late complications after recovery from the acute phase of COVID-19 in emergency medical technicians (EMTs). Methods: This is a cross-sectional case-series study performed during the last quarter of 2020 in Tehran, Iran. All EMTs who had been diagnosed with COVID-19 were eligible. The researcher contacted the EMTs via telephone to determine any complications following their recovery. Those who suffered from any complication were referred to an internal specialist physician for a detailed history and physical examination. Based on the physician’s opinion, some paraclinical or clinical evaluations were requested to be performed. Results: Four hundred thirty-one confirmed cases and two deaths due to this disease were registered among the Tehran EMS center’s EMTs during the study period. Two hundred thirty-eight EMTs were contacted, and 22.7% of them had at least one persistent/late complication following recovery of the acute phase of COVID-19;of whom, 28 EMTs were visited by an internist and completed the tests. The final participants mentioned seventy-five persistent/late complications. Only one EMT had a residual lesion among those who underwent lung CT scans. There were also some pathological findings in the echocardiographic examination and spirometry. Conclusion: Our study showed that persistent/late-onset complications could likely accompany by COVID-19. © 2022 Bentham Science Publishers.

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