Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 109
Filter
1.
Anat Histol Embryol ; 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2097683

ABSTRACT

Educational technologies in veterinary medicine aim to train veterinarians faster and improve clinical outcomes. COVID-19 pandemic, shifted face-to-face teaching to online, thus, the need to provide effective education remotely was exacerbated. Among recent technology advances for veterinary medical education, extended reality (XR) is a promising teaching tool. This study aimed to develop a case resolution approach for radiographic anatomy studies using XR technology and assess students' achievement of differential diagnostic skills. Learning objectives based on Bloom's taxonomy keywords were used to develop four clinical cases (3 dogs/1 cat) of spinal injuries utilizing CT scans and XR models and presented to 22 third-year veterinary medicine students. Quantitative assessment (ASMT) of 7 questions probing 'memorization', 'understanding and application', 'analysis' and 'evaluation' was given before and after contact with XR technology as well as qualitative feedback via a survey. Mean ASMT scores increased during case resolution (pre 51.6% (±37%)/post 60.1% (± 34%); p < 0.01), but without significant difference between cases (Kruskal-Wallis H = 2.18, NS). Learning objectives were examined for six questions (Q1-Q6) across cases (C1-4): Memorization improved sequentially (Q1, 2 8/8), while Understanding and Application (Q3,4) showed the greatest improvement (26.7%-76.9%). Evaluation and Analysis (Q5,6) was somewhat mixed, improving (5/8), no change (3/8) and declining (1/8).Positive student perceptions suggest that case studies' online delivery was well received stimulating learning in diagnostic imaging and anatomy while developing visual-spatial skills that aid understanding cross-sectional images. Therefore, XR technology could be a useful approach to complement radiological instruction in veterinary medicine.

2.
Artificial Intelligence and Machine Learning for EDGE Computing ; : 315-324, 2022.
Article in English | Scopus | ID: covidwho-2060209

ABSTRACT

One of the biggest challenges that the world is facing right now is the identification of COVID-19 infection, given no potential vaccine for the fast-spreading virus. Ongoing insights demonstrate that the number of individuals infected with COVID-19 is expanding exponentially, with more than 40 million confirmed cases around the world. One of the pivotal steps in battling COVID-19 is the capacity to recognize the infected patients sufficiently early and put them under isolation. One of the quickest approaches is to predict the illness from radiography and radiology pictures. Propelled by prior works, I present a machine learning binary classification model-driven deep convolutional neural network to predict COVID-19 from chest X-Ray images. A blend of Dr. Joseph Paul Cohen’s open-sourced database and Kaggle’s Chest X-ray competitions dataset were used to train our model. The predictions result of the model exhibit promising performance with an accuracy of 95.61%. Training and validation accuracy graphs along with training and validation loss graphs were plotted for a better comprehension of our model. Further evaluation of the model was done by calculating standard evaluation metrics where 100% sensitivity, 93.33% specificity, 93.75% precision, and F1-score of 96.77% were achieved. The results exhibit that advanced machine learning methods combined with radiological imaging proved to be a deployable methodology for correct diagnosis of COVID-19, and can likewise be assistive to defeat the issues like shortage of testing kits, time-consuming, and expensive testing methods. © 2022 Elsevier Inc. All rights reserved.

3.
World J Radiol ; 14(9): 342-351, 2022 Sep 28.
Article in English | MEDLINE | ID: covidwho-2055969

ABSTRACT

We suggest an augmentation of the excellent comprehensive review article titled "Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic" under the following categories: (1) "Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia"; (2) "Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis"; (3) "Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including 'intelligent edge' and other remote monitoring devices"; (4) "Artificial intelligence in COVID-19 imaging"; (5) "Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed"; and (6) "A minor correction to a passage on pulmonary destruction".

4.
2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022 ; : 361-367, 2022.
Article in English | Scopus | ID: covidwho-2051930

ABSTRACT

Corona virus was declared a global pandemic that has affected people worldwide. It is critical to diagnose corona virus-infected individuals to restrict the virus's transmission. Recent research indicates that radiological methods provide valuable information in identifying infection using deep learning algorithms. Deep learning has contributed to large-scale medical data research, providing new ways and chances for diagnostic tools. This research attempted to investigate how the Capsule Networks leverage chest X-ray scans to identify the infected person. We suggest Capsule Networks identify the illness using chest X-ray data. The proposed approach is rapid and robust, classifying scans into COVID-19, No Findings, or any other issue in the lungs. The study can be used as a preliminary diagnosis by medical practitioners, and the study focuses on the COVID-19 class, a minority class in all public data sets accessible, and ensures that no COVID-19 infected individual is identified as Normal. Even with a small dataset, the model provides 96.37% accuracy for COVID-19 and for the non-COVID-19, and on multi-class classification, it provides an accuracy of 95.12%. © 2022 IEEE.

5.
Appl Soft Comput ; 129: 109625, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2031137

ABSTRACT

COVID-19 causes an ongoing worldwide pandemic situation. The non-discovery of specialized drugs and/or any other kind of medicines makes the situation worse. Early diagnosis of this disease will be certainly helpful to start the treatment early and also to bring down the dire spread of this highly infectious virus. This article describes the proposed novel unsupervised segmentation method to segment the radiological image samples of the chest area that are accumulated from the COVID-19 infected patients. The proposed approach is helpful for physicians, medical technologists, and other related experts in the quick and early diagnosis of COVID-19 infection. The proposed approach will be the SUFEMO (SUperpixel based Fuzzy Electromagnetism-like Optimization). This approach is developed depending on some well-known theories like the Electromagnetism-like optimization algorithm, the type-2 fuzzy logic, and the superpixels. The proposed approach brings down the processing burden that is required to deal with a considerably large amount of spatial information by assimilating the notion of the superpixel. In this work, the EMO approach is modified by utilizing the type 2 fuzzy framework. The EMO approach updates the cluster centers without using the cluster center updation equation. This approach is independent of the choice of the initial cluster centers. To decrease the related computational overhead of handling a lot of spatial data, a novel superpixel-based approach is proposed in which the noise-sensitiveness of the watershed-based superpixel formation approach is dealt with by computing the nearby minima from the gradient image. Also, to take advantage of the superpixels, the fuzzy objective function is modified. The proposed approach was evaluated using both qualitatively and quantitatively using 310 chest CT scan images that are gathered from various sources. Four standard cluster validity indices are taken into consideration to quantify the results. It is observed that the proposed approach gives better performance compared to some of the state-of-the-art approaches in terms of both qualitative and quantitative outcomes. On average, the proposed approach attains Davies-Bouldin index value of 1.812008792, Xie-Beni index value of 1.683281, Dunn index value 2.588595748, and ß index value 3.142069236 for 5 clusters. Apart from this, the proposed approach is also found to be superior with regard to the rate of convergence. Rigorous experiments prove the effectiveness of the proposed approach and establish the real-life applicability of the proposed method for the initial filtering of the COVID-19 patients.

6.
Medical Journal of Dr. D.Y. Patil Vidyapeeth ; 15(7):S100-S102, 2022.
Article in English | Scopus | ID: covidwho-2024845

ABSTRACT

Classical high-resolution computed tomography (HRCT) pattern in nCOVID-19 pneumonia is bilateral, basal, peripheral, subpleural, bronchopneumonia. Ground-glass opacities and consolidation are the most common initial radiological findings. However, chest computed tomography (CT) should not be used as an independent diagnostic tool to exclude or confirm COVID-19. CT is not a standard diagnostic tool for the diagnosis of COVID-19, but CT findings help to suggest the diagnosis in the appropriate setting. Chest CT findings should be correlated with epidemiologic history, clinical presentation, and reverse transcriptase-polymerase chain reaction (RT-PCR) test results. Many other diseases can mimic nCOVID-19 in HRCT and vice versa. We report an atypical radiological feature in RT-PCR-confirmed nCOVID-19 pneumonia case. HRCT showed unilateral peripheral ground-glass opacity. Atypical HRCT features in nCOVID-19 described in literature include central involvement, peribronchovascular involvement, isolated upper lobe involvement, nodular opacities, lobar consolidation, solitary opacity, unilateral lung involvement, mediastinal adenopathy, cavitory lesions, pleural and pericardial effusion, and subpleural sparing. When radiological manifestations are atypical, diagnosis of nCOVID-19 pneumonia should be by exclusion of other causes for the radiological abnormality. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.

7.
Bratisl Lek Listy ; 123(9): 653-6958, 2022.
Article in English | MEDLINE | ID: covidwho-2024875

ABSTRACT

PURPOSE: In addition to the highly variable clinical presentation of acute COVID-19 infection, it can also cause various post-acute signs and symptoms. In our study, we aimed to examine the efficacy of anti-fibrotic therapy in patients who developed pulmonary fibrosis after COVID-19. METHODS: In total, 15 patients who applied to the Post-Covid Outpatient Clinic between May 2021 and August 2021 and were diagnosed with COVID-19 pneumonia, and whose cough, dyspnea, exertional dyspnea and low saturation continued to be present at least 12 weeks after the diagnosis, were included in the study. Off-label pirfenidone treatment was started according to the radiological findings, pulmonary function test parameters (PFT) and 6-minute walking test (6MWT) results. The patients were followed up for 12 weeks. RESULTS: While all of the FVC, FVC%, FEV1, FEV1%, DLCO%, DLCO/VA%, 6MWT, and room air saturation levels were observed to increase statistically significantly in the patients at the 12th week, it was determined that there was a statistically significant decrease in the pulse level in room air (p = 0.01, 0.01, 0.01, 0.01, 0.004, 0.001, 0.002, 0.001, and 0.002, respectively). In regression analysis based on radiological scoring, it was observed that the DLCO and room air saturation levels at the 12th week of the treatment were statistically significantly higher in patients with lower scores at the beginning (p = 0.04, 0.03). In addition, it was observed that anti-fibrotic treatment, which was started in the earliest period, i.e., 12 weeks after the diagnosis, resulted in an improvement in radiological, PFT and 6MWT parameters. CONCLUSION: Patients who still had dyspnea and low saturation 12 weeks after the diagnosis, defined as chronic COVID-19, should be evaluated for anti-fibrotic therapy after the necessary radiological and PFT evaluation. Early treatment commencement brings about, besides radiological improvement, a better response obtained in PFT and 6MWT (Tab. 2, Fig. 2, Ref. 21).


Subject(s)
COVID-19 , Pulmonary Fibrosis , COVID-19/drug therapy , Dyspnea/etiology , Humans , Pulmonary Fibrosis/complications , Pulmonary Fibrosis/drug therapy , Respiratory Function Tests/adverse effects
8.
1st International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022 ; : 447-452, 2022.
Article in English | Scopus | ID: covidwho-2018633

ABSTRACT

COVID-19 (novel coronavirus disease) is a deadly illness, has infected and killed a very large number of people worldwide. The widely followed lab testing (RT-PCR Test) for the detection of this disease has various limitations with high cost and take long time to provide the outcome. As a result, diverse technologies that permit for the quick and accurate finding of the infection can provide much required assistance to medical management. In recent studies, gained radiological imaging techniques, such images convey important information about this virus. Advanced Deep learning (DL) techniques combined with the radiology images can aid in the correct diagnosis of the virus, as well as defeat the problem of insufficient expert physicians in rural areas. In this work, aimed at presenting a DL based-Convolutional neural network (CNN) model for the automatic detection of the coronavirus from X-ray images of chest. The Kaggle dataset available publicly of total 42330 images from 4-categories are used. The experiment produced the accuracy of 88.53% and 86.19% for training and validation, which is better result for the highest number of radiographic images in comparison to existing work. © 2022 IEEE.

9.
21st International Conference on Image Analysis and Processing , ICIAP 2022 ; 13374 LNCS:508-519, 2022.
Article in English | Scopus | ID: covidwho-2013964

ABSTRACT

The outbreak of the COVID-19 pandemic considerably increased the workload in hospitals. In this context, the availability of proper diagnostic tools is very important in the fight against this virus. Scientific research is constantly making its contribution in this direction. Actually, there are many scientific initiatives including challenges that require to develop deep algorithms that analyse X-ray or Computer Tomography (CT) images of lungs. One of these concerns a challenge whose topic is the prediction of the percentage of COVID-19 infection in chest CT images. In this paper, we present our contribution to the COVID-19 Infection Percentage Estimation Competition organised in conjunction with the ICIAP 2021 Conference. The proposed method employs algorithms for classification problems such as Inception-v3 and the technique of data augmentation mixup on COVID-19 images. Moreover, the mixup methodology is applied for the first time in radiological images of lungs affected by COVID-19 infection, with the aim to infer the infection degree with slice-level precision. Our approach achieved promising results despite the specific constrains defined by the rules of the challenge, in which our solution entered in the final ranking. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

10.
Int J Environ Res Public Health ; 19(17)2022 Aug 26.
Article in English | MEDLINE | ID: covidwho-2006013

ABSTRACT

BACKGROUND: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.


Subject(s)
COVID-19 , Algorithms , COVID-19/diagnostic imaging , Humans , Machine Learning , Neural Networks, Computer , Tomography, X-Ray Computed/methods
11.
Radioprotection ; 57(3):217-231, 2022.
Article in English | Web of Science | ID: covidwho-2004806

ABSTRACT

As COVID-19 emerged, there are parallels between the responses needed for managing SARS-CoV-2 infections and radiation injuries. While some SARS-CoV-2-infected individuals present as asymptomatic, others exhibit a range of symptoms including severe and rapid onset of high-risk indicators of mortality. Similarly, a variety of responses are also observed after a radiological exposure depending on radiation dose, dose heterogeneity, and biological variability. The impact of acute radiation syndrome (ARS) has guided the identification of many biomarkers of radiation exposure, the establishment of medical management strategies, and development of medical countermeasures in the event of a radiation public health emergency. Biodosimetry has a prominent role for identifying exposed persons during a large scale radiological emergency situation. Identifying exposed individuals is also critical in the case of pandemics such as COVID-19, with the additional goal of controlling the spread of disease. Conclusions and significance: IABERD has taken advantage of its competences in biodosimetry to draw lessons from current practices of managing the testing strategy for nuclear accidents to improve responses to SARS-CoV-2. Conversely, lessons learned from managing SARS-CoV-2 can be used to inform best practices in managing radiological situations. Finally, the potential need to deal with testing modalities simultaneously and effectively in both situations is considered.

12.
Radioprotection ; 57(3):233-240, 2022.
Article in English | Web of Science | ID: covidwho-2004805

ABSTRACT

The pandemic situation, originated due to the appearance of the SARS-CoV-2 virus, changed many aspects of our lives and jobs. This health crisis also affected the day-to-day work of radiation protection experts, including the wide range of areas involved in this sector. This study aimed to evaluate the impact of this pandemic on the Spanish radiation protection experts. For that purpose, a Google Forms online survey was developed with 39 questions. The survey covered different aspects related to the work developed by the Spanish professionals, taking into account three different time periods along the pandemic situation. According to this survey, the appearance of COVID-19 modified the labour conditions and modalities of many Spanish radiation protection professionals, especially at the beginning of the pandemic. Most on-site activities were related to the health sector and the nuclear industry, other sectors were more flexible, and the workload increased for half of the surveyed participants. Many operational activities suffered delays due to the pandemic, whereas the one-month wearing period of passive personal dosimeters was extended in most cases during the first Spanish alarm state (15th March 2020 up to the 21st June 2020). Finally, difficulties faced in terms of the working area have been identified and may be useful for the future.

13.
Multiple Sclerosis and Related Disorders ; 59, 2022.
Article in English | EMBASE | ID: covidwho-2004360

ABSTRACT

Background: CNS involvement in CLL is rare and it usually occurs in late-stage CLL disease. There is usual delay in the diagnosis due to its variable manifestations, challenging diagnosis process and possible misdiagnosis with a mimicker condition. I am sharing our relative successful experience with this challenging case that had satisfied outcome after going through comprehensive investigations and treatment journey treating his symptoms until arriving the final diagnosis and getting the best treatment option. Material(s) and Method(s): A 42 years old male, with recent COVID-19 infection, presented with multiple progressive neurologic symptoms over one month;started as numbness around the mouth, reduced facial sensation and a feeling of band like sensation below the costal margins. On exam, he had left abduction restriction, diplopia on left gaze and upbeat nystagmus, reduced facial sensation and hyperesthesia. The reflexes were 1+ in the upper limbs, 3+ in the lower limbs, up going planters, tingling from the feet up to T6 level and postural tremor bilaterally. His CSF showed high protein level. MRI brain/ spine revealed left frontal juxtacortical white matter and bilateral middle cerebral peduncles lesions with post-contrast enhancement and long segment spinal cord demyelinating plaques. He was initially treated as a case of Acute disseminated encephalomyelitis (ADEM) post viral infection in a background of CLL. The delayed diagnosis was due to temporal relation of neurological manifestation to viral infection, similar MRI lesions to ADEM and multiple negative CSF results of cytology and flow cytometry. He had persistent disabling symptoms and enhancing lesions in MRI despite being treated with IVMP, IVIG and PLEX. He was managed for ADEM based on responsiveness to the recommended therapy step by step. Firstly, he received a high-dose corticosteroids, secondly IV immunoglobulin but he was still progressing and considered as steroid-unresponsive ADEM. lastly, plasma exchange was done when he exhibited progressive symptoms with fair improvement. Interestingly, the patient showed significant improvement in the clinical and radiological parameters after starting him with a new anti-leukemia medication (Acalabrutinib) for his concurrent active condition. He run out of his medication for around 1 week and he experienced recurrent of the neurological manifestation and the previous lesions in the images. A repeated flow cytometry for the third time came positive for CLL cells and the final diagnosis of CNS involvement by CLL was established. The diagnosis was made after the exclusion of other etiologies. Result(s): The patient received Ibrutinib at a standard dose and as a monotherapy. It is an efficient chemotherapy that crosses the blood brain barrier and has showed a favorable clinical, biological and radiological outcome. The patient is back to his work and his daily activities have improved. Conclusion(s): In case of inconclusive work up, CSF analysis should be repeated testing for cytology and flow cytometry\immunophenotypes as the false negative results are common. Our patient had an active CLL proved in his investigations, and the fact that the patient responded very well to the new chemotherapy should alert the diagnosis of CNS involvement by CLL and directs towards repeating investigations and introducing aggressive treatment strategy to target both hematological and neurological complications of the condition.

14.
Journal of Health Sciences and Surveillance System ; 10(3):276-283, 2022.
Article in English | Scopus | ID: covidwho-1988944

ABSTRACT

Background: Patients with COVID-19 (coronavirus disease 2019) present varying disease severity;with such heterogeneity in clinical presentations, it can be challenging to assess the severity and progression of the disease. In addition, no specific markers have been identified that would indicate the diagnosis or prognosis of the disease. Therefore, this study was aimed to determine whether a panel of hematological and inflammatory biomarkers were indicative of disease severity in the assessment and the prognosis of COVID-19. Methods: The retrospective cross-sectional study was carried out in a university hospital in South India between May 2020 and September 2020. The participants were 997 patients with COVID-19, confirmed by real-time reverse transcriptasepolymerase chain reaction (RT-PCR). Information regarding demographics and laboratory tests was obtained from medical records. Association analysis was conducted using SPSS, version 16, and a P-value <0.05 was considered statistically significant. Results: Inflammatory markers such as C-reactive protein (CRP) and D-dimer, calculated inflammatory ratios, and hemoglobin were significantly increased in cases of severe COVID-19. Leucocytosis with increased absolute neutrophil count and decreased absolute lymphocyte count were observed. Conclusion: Haematological and inflammatory markers may indicate the severity of the disease. The severity of COVID-19 was indicated by elevated total white cells, increased neutrophillymphocyte, and platelet-lymphocyte ratios. Increasing levels of CRP indicated a severe prognosis of the disease. D-dimer elevations may indicate the incidence of thromboembolic episodes. Therefore, hematological indices were considered applicable in assessing the progression of the disease and for the risk stratification of the disease. © 2022 Shriaz University of Medical Sciences

15.
Respirology ; 27(12): 1073-1082, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1978519

ABSTRACT

BACKGROUND AND OBJECTIVE: COVID-19 remains a major cause of respiratory failure, and means to identify future deterioration is needed. We recently developed a prediction score based on breath-holding manoeuvres (desaturation and maximal duration) to predict incident adverse COVID-19 outcomes. Here we prospectively validated our breath-holding prediction score in COVID-19 patients, and assessed associations with radiological scores of pulmonary involvement. METHODS: Hospitalized COVID-19 patients (N = 110, three recruitment centres) performed breath-holds at admission to provide a prediction score (Messineo et al.) based on mean desaturation (20-s breath-holds) and maximal breath-hold duration, plus baseline saturation, body mass index and cardiovascular disease. Odds ratios for incident adverse outcomes (composite of bi-level ventilatory support, ICU admission and death) were described for patients with versus without elevated scores (>0). Regression examined associations with chest x-ray (Brixia score) and computed tomography (CT; 3D-software quantification). Additional comparisons were made with the previously-validated '4C-score'. RESULTS: Elevated prediction score was associated with adverse COVID-19 outcomes (N = 12/110), OR[95%CI] = 4.54[1.17-17.83], p = 0.030 (positive predictive value = 9/48, negative predictive value = 59/62). Results were diminished with removal of mean desaturation from the prediction score (OR = 3.30[0.93-11.72]). The prediction score rose linearly with Brixia score (ß[95%CI] = 0.13[0.02-0.23], p = 0.026, N = 103) and CT-based quantification (ß = 1.02[0.39-1.65], p = 0.002, N = 45). Mean desaturation was also associated with both radiological assessment. Elevated 4C-scores (≥high-risk category) had a weaker association with adverse outcomes (OR = 2.44[0.62-9.56]). CONCLUSION: An elevated breath-holding prediction score is associated with almost five-fold increased adverse COVID-19 outcome risk, and with pulmonary deficits observed in chest imaging. Breath-holding may identify COVID-19 patients at risk of future respiratory failure.


Subject(s)
COVID-19 , Respiratory Insufficiency , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Lung/diagnostic imaging , Tomography, X-Ray Computed , Respiratory Insufficiency/diagnostic imaging , Respiratory Insufficiency/epidemiology , Retrospective Studies
16.
Current Respiratory Medicine Reviews ; 18(2):121-133, 2022.
Article in English | Scopus | ID: covidwho-1963205

ABSTRACT

Background: COVID-19 has still been expressed as a mysterious viral infection with dramatic pulmonary consequences. Objectives: This article aims to study the radiological pulmonary consequences of respiratory covid-19 infection at 6 months and their relevance to the clinical stage, laboratory markers, and management modalities. Methods: This study was implemented on two hundred and fifty (250) confirmed positive cases for COVID-19 infections. One hundred and ninety-seven cases (197) who completed the study displayed residual radiological lung shadowing (RRLS) on follow-up computed tomography (CT) of the chest. They were categorized by Simple clinical classification of COVID-19 into groups A, B and C. Results: GGO, as well as reticulations, were statistically significantly higher in group A than the other two groups;however, bronchiectasis changes, parenchymal scarring, nodules as well as pleural tractions were statistically significantly higher in group C than the other two groups. Conclusion: Respiratory covid-19 infection might be linked to residual radiological lung shadowing. Ground glass opacities GGO, reticulations pervaded in mild involvement with lower inflammatory markers level, unlike, severe changes that expressed scarring, nodules and bronchiectasis changes accompanied by increased levels of inflammatory markers. © 2022 Bentham Science Publishers.

17.
2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2021 ; 898:35-57, 2022.
Article in English | Scopus | ID: covidwho-1958936

ABSTRACT

New Coronavirus 2019 (COVID-19) is a virus that causes severe pneumonia and affects many organs of the body. This infection was initially discovered in one of the cities in the Republic of China, Wuhan, in December 2019 and since then has been spread throughout the globe as a global pandemic. To prevent the virus from spreading, positive cases must be identified early and infected persons must be treated as soon as possible. As new instances emerge regularly, many developing countries are experiencing COVID-19 testing kit scarcity because the demand for testing kits has soared. As an alternative, radiological imaging techniques such as X-ray images have been proven to help in COVID-19 diagnosis because images from X-ray provide valuable information about the COVID-19 virus disease. This paper presents a survey of Deep learning-based methods in identifying COVID-19 with X-ray input images, and classifies these images into several categories, namely: no findings, normal, COVID, and pneumonia. Several studies have been included with details about their datasets, methodologies, and findings. A total of thirteen popular datasets and fifteen articles are reviewed in this paper. Research challenges and recommendations for future research directions are also provided as an evaluation of previous research. Search for research articles in well-known digital libraries, namely Scopus, IEEE Xplore, Springer, and ScienceDirect, was carried out to obtain a list of studies relevant to the scope of research. Related articles that have a high impact are considered in the list of studies. Also, in selecting studies related to the research scope, we apply some inclusion and exclusion criteria. The list of studies used in subsequent research is imported to the library. Then, studies that did not match the criteria for inclusion were eliminated. The clinical application of artificial intelligence, i.e., DL in diagnosing COVID-19, is promising, and further research is needed. Convolutional Neural Network (CNN) approaches could be used in collaboration through X-ray pictures to identify diseases quickly and accurately, reducing the shortage of testing equipment and their restrictions. It is expected that this work can help researchers understand the general picture and existing research gaps to decide on the appropriate architecture and approach in developing deep learning-based covid identification research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

18.
American Journal of Stem Cells ; 11(3):37-55, 2022.
Article in English | EMBASE | ID: covidwho-1955743

ABSTRACT

Objective: Mesenchymal stem cells can serve as a therapeutic option for COVID-19. Their immunomodula-tory and anti-inflammatory properties can regulate the exaggerated inflammatory response and promote recovery of lung damage. Method: Phase-1, single-centre open-label, prospective clinical trial was conducted to evaluate the safety and efficacy of intravenous administration of mesenchymal stem cells derived from umbilical cord and placenta in moderate COVID-19. The study was done in 2 stages with total 20 patients. Herein, the results of stage 1 including first 10 patients receiving 100 million cells on day 1 and 4 with a follow up of 6 months have been discussed. Results: No adverse events were recorded immediately after the administration of MSCs or on follow up. There was no deterioration observed in clinical, laboratory and radiological parameters. All symptoms of the study group resolved within 10 days. Levels of inflammatory biomarkers such as NLR, CRP, IL6, ferritin and D-dimer improved in all patients after intervention along with improved oxygenation demonstrated by improvement in the SpO2/FiO2 ratio and PaO2/FiO2 ratio. None of the patients progressed to severe stage. 9 out of 10 patients were discharged within 9 days of their admission. Improvements were noted in chest x-ray and chest CT scan scores at day 7 in most patients. No post-covid fibrosis was observed on chest CT 28 days after intervention and Chest X ray after 6 months of the intervention. Conclusion: Administration of 100 million mesenchymal stem cells in combina-tion with standard treatment was found to be safe and resulted in prevention of the cytokine storm, halting of the disease progression and acceleration of recovery in moderate COVID-19. This clinical trial has been registered with the Clinical Trial Registry-India (CTRI) as CTRI/2020/08/027043. http://www.ctri.nic.in/Clinicaltrials/pmaindet2. php?trialid=43175.

19.
Front Immunol ; 13: 849560, 2022.
Article in English | MEDLINE | ID: covidwho-1938616

ABSTRACT

Humorally associated autoimmune diseases generally show a female predominance whereas ankylosing spondylitis, a disease that overlaps with psoriatic arthritis (PsA), shows a male predominance. The present review ascertains the current knowledge of sex-specific differences related to psoriatic arthritis (PsA), a chronic, inflammatory condition associated with psoriasis. Sex differences may have important implications for clinical research in PsA and in terms of epidemiology (incidence, prevalence, lifetime risk, survival, and mortality), clinical, radiological, and laboratory features, and response to treatment. While nationwide surveys and large-scale databases and registries show no sex-specific differences, varying male/female ratios have been reported, ranging from 0.42 to 2.75 (comparable with those reported for psoriasis vulgaris: ranging from 0.28 to 2.38). This may reflect subtle, complex, nonlinear interactions between the biological make-up of the individual (genetic and epigenetic differences), hormonal components including menopausal status, environmental exposures including skeletal physical stressing, and psychological variables. There exists methodological heterogeneity and paucity of data concerning sex-specific differences, in terms of the specific population studied, study design, and the diagnostic criteria utilized. Harmonizing and reconciling these discrepancies would be of crucial importance in achieving the ambitious goals of personalized/individualized medicine and further standardized meta-data and Big Data could help disentangle and elucidate the precise mechanisms of underlying potential PsA sex-specific differences.


Subject(s)
Arthritis, Psoriatic , Psoriasis , Spondylitis, Ankylosing , Arthritis, Psoriatic/drug therapy , Female , Humans , Incidence , Male , Spondylitis, Ankylosing/diagnosis
20.
6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022 ; : 1013-1017, 2022.
Article in English | Scopus | ID: covidwho-1922684

ABSTRACT

The novel coronavirus (COVID-2019), which first arrived in the Chinese city of Wuhan in December 2019, unrolled like a wind around the arena and brought into an epidemic and declared as a worldwide pandemic by WHO in march,2020. Agriculture, building, manufacturing, trade, lifestyle, tourism, and the global economy all suffer as a result of this disease. Consequently, it's very crucial to diagnose and treat the disease as soon as possible. According to radiology imaging methodologies radiological imaging techniques may aid in appropriately diagnosing and treating the condition with less response time. The utilization of raw chest X-ray pictures has been used to introduce a new model for the automatic detection of COVID-19 in this investigation. In this work we have built a binary classifier to detect covid-19 by deploying the deep learning technique-CNN on dataset collected from repository of JHU CSSE 2019 and was supported by JHU APL and transfer learning techniques has also been utilized to enhance the dataset. The best classification accuracy achieved by our model on chest X-ray dataset is 98.3%. We have also analyzed and compared the model in other research papers with our model. © 2022 IEEE.

SELECTION OF CITATIONS
SEARCH DETAIL