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1.
Rheumatology international ; 2022.
Article in English | PMC | ID: covidwho-1820920

ABSTRACT

Since the late 1990s, tumor necrosis factor alpha (TNF-α) inhibitors (anti-TNFs) have revolutionized the therapy of immune-mediated inflammatory diseases (IMIDs) affecting the gut, joints, skin and eyes. Although the therapeutic armamentarium in IMIDs is being constantly expanded, anti-TNFs remain the cornerstone of their treatment. During the second decade of their application in clinical practice, a large body of additional knowledge has accumulated regarding various aspects of anti-TNF-α therapy, whereas new indications have been added. Recent experimental studies have shown that anti-TNFs exert their beneficial effects not only by restoring aberrant TNF-mediated immune mechanisms, but also by de-activating pathogenic fibroblast-like mesenchymal cells. Real-world data on millions of patients further confirmed the remarkable efficacy of anti-TNFs. It is now clear that anti-TNFs alter the physical course of inflammatory arthritis and inflammatory bowel disease, leading to inhibition of local and systemic bone loss and to a decline in the number of surgeries for disease-related complications, while anti-TNFs improve morbidity and mortality, acting beneficially also on cardiovascular comorbidities. On the other hand, no new safety signals emerged, whereas anti-TNF-α safety in pregnancy and amid the COVID-19 pandemic was confirmed. The use of biosimilars was associated with cost reductions making anti-TNFs more widely available. Moreover, the current implementation of the "treat-to-target" approach and treatment de-escalation strategies of IMIDs were based on anti-TNFs. An intensive search to discover biomarkers to optimize response to anti-TNF-α treatment is currently ongoing. Finally, selective targeting of TNF-α receptors, new forms of anti-TNFs and combinations with other agents, are being tested in clinical trials and will probably expand the spectrum of TNF-α inhibition as a therapeutic strategy for IMIDs.

2.
Diagnostics (Basel) ; 12(3)2022 Mar 16.
Article in English | MEDLINE | ID: covidwho-1760432

ABSTRACT

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

3.
J Korean Med Sci ; 36(50): e338, 2021 Dec 27.
Article in English | MEDLINE | ID: covidwho-1596045

ABSTRACT

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).


Subject(s)
COVID-19/drug therapy , Research Design , SARS-CoV-2 , COVID-19/epidemiology , Ethics, Research , Humans , Peer Review , Pilot Projects , Publishing
4.
Diagnostics (Basel) ; 11(12)2021 Dec 15.
Article in English | MEDLINE | ID: covidwho-1572404

ABSTRACT

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

5.
Mediterr J Rheumatol ; 31(Suppl 2): 284-287, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-1554689

ABSTRACT

In response to the COVID-19 pandemic, many countries have adopted community containment to manage COVID-19. These measures to reduce human contact, such as social distancing, are deemed necessary to contain the spread of the virus and protect those at increased risk of developing complications following infection with COVID-19. People with rheumatoid arthritis (RA) are advised to adhere to even more stringent restrictions compared to the general population, and avoid any social contact with people outside their household. This social isolation combined with the anxiety and stress associated with the pandemic, is likely to particularly have an impact on mental health and psychological wellbeing in people with RA. Increasing physical activity and reducing sedentary behaviour can improve mental health and psychological wellbeing in RA. However, COVID-19 restrictions make it more difficult for people with RA to be physically active and facilitate a more sedentary lifestyle. Therefore, guidance is necessary for people with RA to adopt a healthy lifestyle within the constraints of COVID-19 restrictions to support their mental health and psychological wellbeing during and after the COVID-19 pandemic.

6.
Front Biosci (Landmark Ed) ; 26(11): 1312-1339, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1552205

ABSTRACT

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.


Subject(s)
Arteries/diagnostic imaging , Atherosclerosis/diagnostic imaging , COVID-19/physiopathology , Cardiovascular Diseases/diagnostic imaging , Nutritional Status , Algorithms , COVID-19/diagnostic imaging , COVID-19/virology , Humans , Risk Factors , SARS-CoV-2/isolation & purification
7.
Diagnostics (Basel) ; 11(11)2021 Nov 01.
Article in English | MEDLINE | ID: covidwho-1488513

ABSTRACT

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

8.
Mediterr J Rheumatol ; 31(Suppl 2): 243-246, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-1410320

ABSTRACT

The flow of information on Coronavirus Disease 2019 (COVID-19) is intensifying, requiring concerted efforts of all scholars. Peer-reviewed journals as established channels of scientific communications are struggling to keep up with unprecedented high submission rates. Preprint servers are becoming increasingly popular among researchers and authors who set priority over their ideas and research data by pre-publication archiving of their manuscripts on these professional platforms. Most published articles on COVID-19 are now archived by the PubMed Central repository and available for searches on LitCovid, which is a newly designed hub for specialist searches on the subject. Social media platforms are also gaining momentum as channels for rapid dissemination of COVID-19 information. Monitoring, evaluating and filtering information flow through the established and emerging scholarly platforms may improve the situation with the pandemic and save lives.

9.
Diagnostics (Basel) ; 11(8)2021 Aug 04.
Article in English | MEDLINE | ID: covidwho-1341653

ABSTRACT

BACKGROUND: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. METHODOLOGY: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. RESULTS: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. CONCLUSIONS: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

10.
World J Diabetes ; 12(3): 215-237, 2021 Mar 15.
Article in English | MEDLINE | ID: covidwho-1148329

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

11.
Comput Biol Med ; 130: 104210, 2021 03.
Article in English | MEDLINE | ID: covidwho-1064978

ABSTRACT

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.


Subject(s)
Artificial Intelligence , COVID-19/diagnostic imaging , Lung/diagnostic imaging , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , Humans
12.
Int J Comput Assist Radiol Surg ; 16(3): 423-434, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1061143

ABSTRACT

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.


Subject(s)
Artificial Intelligence , COVID-19/diagnostic imaging , Lung/diagnostic imaging , Pneumonia/diagnostic imaging , Deep Learning , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed/methods
13.
Rev Cardiovasc Med ; 21(4): 541-560, 2020 12 30.
Article in English | MEDLINE | ID: covidwho-1059479

ABSTRACT

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.


Subject(s)
Artificial Intelligence , COVID-19/epidemiology , Cardiovascular Diseases/epidemiology , Delivery of Health Care/methods , Pandemics , Risk Assessment , SARS-CoV-2 , Cardiovascular Diseases/therapy , Comorbidity , Humans , Risk Factors
14.
Rheumatol Int ; 41(2): 335-344, 2021 02.
Article in English | MEDLINE | ID: covidwho-951631

ABSTRACT

Nationwide lockdowns during SARS-CoV-2 (COVID-19) can compromise mental health and psychological wellbeing and limit opportunities for physical activity (PA), particularly in clinical populations, such as people with rheumatoid arthritis (RA), who are considered at risk for COVID-19 complications. This study aimed to investigate associations between PA and sedentary time (ST) with indicators of mental health and wellbeing in RA during COVID-19 lockdown, and examine the moderation effects of self-isolating. 345 RA patients completed an online questionnaire measuring PA (NIH-AARP Diet and Health Study Questionnaire), ST (International Physical Activity Questionnaire-Short Form), pain (McGill Pain Questionnaire and Visual Analogue Scale), fatigue (Multidimensional Fatigue Inventory), depressive and anxious symptoms (Hospital Anxiety and Depression Scale), and vitality (Subjective Vitality Scale) during the United Kingdom COVID-19 lockdown. Associations between PA and ST with mental health and wellbeing were examined using hierarchical multiple linear regressions. Light PA (LPA) was significantly negatively associated with mental fatigue (ß = - .11), depressive symptoms (ß = - .14), and positively with vitality (ß = .13). Walking was negatively related to physical fatigue (ß = - .11) and depressive symptoms (ß = - .12) and positively with vitality (ß = .15). Exercise was negatively associated with physical (ß = - .19) and general (ß = - .12) fatigue and depressive symptoms (ß = - .09). ST was positively associated with physical fatigue (ß = .19). Moderation analyses showed that LPA was related to lower mental fatigue and better vitality in people not self-isolating, and walking with lower physical fatigue in people self-isolating. These findings show the importance of encouraging PA for people with RA during a lockdown period for mental health and wellbeing.


Subject(s)
Arthritis, Rheumatoid/psychology , COVID-19 , Exercise , Aged , Anxiety/psychology , Communicable Disease Control , Depression/psychology , Female , Humans , Male , Middle Aged , Pandemics , Physical Distancing , SARS-CoV-2 , Sedentary Behavior , Surveys and Questionnaires
15.
Rheumatol Int ; 40(9): 1353-1360, 2020 09.
Article in English | MEDLINE | ID: covidwho-640396

ABSTRACT

As of June 10th 2020 about 7.2 million individuals have tested positive for, and more than 410,000 have died due to COVID-19. In this review we outline the pathophysiology that underpins the potential use of anti-rheumatic therapies for severe COVID-19 infection and summarize the current evidence regarding the risk and outcome of COVID-19 in patients with systemic autoimmune diseases. Thus far there is no convincing evidence that any disease-modifying anti-rheumatic drug (conventional synthetic, biologic or targeted synthetic) including hydroxychloroquine, may protect against severe COVID-19 infection; answers about their possible usefulness in the management of the cytokine storm associated with severe COVID-9 infection will only arise from ongoing randomized controlled trials. Evidence on COVID-19 risk and outcome in patients with systemic autoimmune diseases is extremely limited; thus, any conclusions would be unsafe and should be seen with great caution. At present, the risk and severity (hospitalization, intensive care unit admission and death) of COVID-19 infection in people with autoimmune diseases do not appear particularly dissimilar to the general population, with the possible exception of hospitalization in patients exposed to high glucocorticoid doses. At this stage it is impossible to draw any conclusions for differences in COVID-19 risk and outcome between different autoimmune diseases and between the various immunomodulatory therapies used for them. More research in the field is obviously required, including as a minimum careful and systematic epidemiology and appropriately controlled clinical trials.


Subject(s)
Antirheumatic Agents/therapeutic use , Autoimmune Diseases/drug therapy , Coronavirus Infections/drug therapy , Cytokine Release Syndrome/drug therapy , Pneumonia, Viral/drug therapy , Antibodies, Monoclonal, Humanized/therapeutic use , Autoimmune Diseases/epidemiology , Autoimmune Diseases/immunology , Betacoronavirus , COVID-19 , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/immunology , Cytokine Release Syndrome/immunology , Humans , Janus Kinase Inhibitors/therapeutic use , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/immunology , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Tumor Necrosis Factor Inhibitors/therapeutic use
16.
Comput Biol Med ; 124: 103960, 2020 09.
Article in English | MEDLINE | ID: covidwho-714312

ABSTRACT

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.


Subject(s)
Betacoronavirus , Brain Injuries/epidemiology , Coronavirus Infections/epidemiology , Heart Injuries/epidemiology , Pneumonia, Viral/epidemiology , Artificial Intelligence , Betacoronavirus/pathogenicity , Betacoronavirus/physiology , Brain Injuries/classification , Brain Injuries/diagnostic imaging , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/methods , Comorbidity , Computational Biology , Coronavirus Infections/classification , Coronavirus Infections/diagnosis , Coronavirus Infections/diagnostic imaging , Deep Learning , Heart Injuries/classification , Heart Injuries/diagnostic imaging , Humans , Machine Learning , Pandemics/classification , Pneumonia, Viral/classification , Pneumonia, Viral/diagnostic imaging , Risk Factors , SARS-CoV-2 , Severity of Illness Index
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