Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome

Evaluating Acute Pulmonary Changes in Coronavirus Disease 2019: A Comparative Analysis of Computed Tomography, Chest Radiography, Lung Ultrasound, Magnetic Resonance Imaging, and Positron Emission Tomography with Fluorodeoxyglucose Modalities

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks

Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value <= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value <= 0.006). However, the performance was still significantly less than the deep model (p-value <= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.

Quantitative Evaluation of COVID-19 Pneumonia CT Using AI Analysis-Feasibility and Differentiation from Other Common Pneumonia Forms

PURPOSE: To implement the technical feasibility of an AI-based software prototype optimized for the detection of COVID-19 pneumonia in CT datasets of the lung and the differentiation between other etiologies of pneumonia. METHODS: This single-center retrospective case-control-study consecutively yielded 144 patients (58 female, mean age 57.72 ± 18.25 y) with CT datasets of the lung. Subgroups including confirmed bacterial (n = 24, 16.6%), viral (n = 52, 36.1%), or fungal (n = 25, 16.6%) pneumonia and (n = 43, 30.7%) patients without detected pneumonia (comparison group) were evaluated using the AI-based Pneumonia Analysis prototype. Scoring (extent, etiology) was compared to reader assessment. RESULTS: The software achieved an optimal sensitivity of 80.8% with a specificity of 50% for the detection of COVID-19; however, the human radiologist achieved optimal sensitivity of 80.8% and a specificity of 97.2%. The mean postprocessing time was 7.61 ± 4.22 min. The use of a contrast agent did not influence the results of the software (p = 0.81). The mean evaluated COVID-19 probability is 0.80 ± 0.36 significantly higher in COVID-19 patients than in patients with fungal pneumonia (p < 0.05) and bacterial pneumonia (p < 0.001). The mean percentage of opacity (PO) and percentage of high opacity (PHO ≥ -200 HU) were significantly higher in COVID-19 patients than in healthy patients. However, the total mean HU in COVID-19 patients was -679.57 ± 112.72, which is significantly higher than in the healthy control group (p < 0.001). CONCLUSION: The detection and quantification of pneumonia beyond the primarily trained COVID-19 datasets is possible and shows comparable results for COVID-19 pneumonia to an experienced reader. The advantages are the fast, automated segmentation and quantification of the pneumonia foci.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier

Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.

Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT.

Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists.

Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy.

Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey

The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.

Clinical course and outcomes of COVID-19 patients with chronic obstructive pulmonary disease: A retrospective observational study in Wuhan, China

Information about coronavirus disease 2019 (COVID-19) patients with pre-existing chronic obstructive pulmonary disease (COPD) is still lacking. The aim of this study is to describe the clinical course and the outcome of COVID-19 patients with comorbid COPD.This retrospective study was performed at Wuhan Huoshenshan Hospital in China. Patients with a clear diagnosis of COVID-19 who had comorbid COPD (N = 78) were identified. COVID-19 patients without COPD were randomly selected and matched by age and sex to those with COPD. Clinical data were analyzed and compared between the two groups. The composite outcome was the onset of intensive care unit admission, use of mechanical ventilation, or death during hospitalization. Multivariable Cox regression analyses controlling for comorbidities were performed to explore the relationship between comorbid COPD and clinical outcome of COVID-19.Compared to age- and sex-matched COVID-19 patients without pre-existing COPD, patients with pre-existing COPD were more likely to present with dyspnea, necessitate expectorants, sedatives, and mechanical ventilation, suggesting the existence of acute exacerbations of COPD (AECOPD). Greater proportions of patients with COPD developed respiratory failure and yielded poor clinical outcomes. However, laboratory tests did not show severer infection, over-activated inflammatory responses, and multi-organ injury in patients with COPD. Kaplan-Meier analyses showed patients with COPD exhibited longer viral clearance time in the respiratory tract. Multifactor regression analysis showed COPD was independently correlated with poor clinical outcomes.COVID-19 patients with pre-existing COPD are more vulnerable to AECOPD and subsequent respiratory failure, which is the main culprit for unfavorable clinical outcomes. However, COPD pathophysiology itself is not associated with over-activated inflammation status seen in severe COVID-19.

Impact of the "atherosclerotic pabulum" on in-hospital mortality for SARS-CoV-2 infection. Is calcium score able to identify at-risk patients?

Background

Although the primary cause of death in COVID‐19 infection is respiratory failure, there is evidence that cardiac manifestations may contribute to overall mortality and can even be the primary cause of death. More importantly, it is recognized that COVID‐19 is associated with a high incidence of thrombotic complications.

Hypothesis

Evaluate if the coronary artery calcium (CAC) score was useful to predict in‐hospital (in‐H) mortality in patients with COVID‐19. Secondary end‐points were needed for mechanical ventilation and intensive care unit admission.

Methods

Two‐hundred eighty‐four patients (63, 25 years, 67% male) with proven severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection who had a noncontrast chest computed tomography were analyzed for CAC score. Clinical and radiological data were retrieved.

Results

Patients with CAC had a higher inflammatory burden at admission (d‐dimer, p = .002; C‐reactive protein, p = .002; procalcitonin, p = .016) and a higher high‐sensitive cardiac troponin I (HScTnI, p = <.001) at admission and at peak. While there was no association with presence of lung consolidation and ground‐glass opacities, patients with CAC had higher incidence of bilateral infiltration (p = .043) and higher in‐H mortality (p = .048). On the other side, peak HScTnI >200 ng/dl was a better determinant of all outcomes in both univariate (p = <.001) and multivariate analysis (p = <.001).

Conclusion

The main finding of our research is that CAC was positively related to in‐H mortality, but it did not completely identify all the population at risk of events in the setting of COVID‐19 patients. This raises the possibility that other factors, including the presence of soft, unstable plaques, may have a role in adverse outcomes in SARS‐CoV‐2 infection.

[Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients

Background

Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients' complexity.

Purpose

To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity.

Materials and Methods

We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC).

Results

Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices.

Conclusion

In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.

Lung ultrasound in predicting COVID-19 clinical outcomes: A prospective observational study

STUDY OBJECTIVE

We sought to determine the ability of lung point-of-care ultrasound (POCUS) to predict mechanical ventilation and in-hospital mortality in patients with coronavirus disease 2019 (COVID-19).

METHODS

This was a prospective observational study of a convenience sample of patients with confirmed COVID-19 presenting to 2 tertiary hospital emergency departments (EDs) in Iran between March and April 2020. An emergency physician attending sonographer performed a 12-zone bilateral lung ultrasound in all patients. Research associates followed the patients on their clinical course. We determined the frequency of positive POCUS findings, the geographic distribution of lung involvement, and lung severity scores. We used multivariable logistic regression to associate lung POCUS findings with clinical outcomes.

RESULTS

A total of 125 patients with COVID-like symptoms were included, including 109 with confirmed COVID-19. Among the included patients, 33 (30.3%) patients were intubated, and in-hospital mortality was reported in 19 (17.4%). Lung POCUS findings included pleural thickening 95.4%, B-lines 90.8%, subpleural consolidation 86.2%, consolidation 46.8%, effusions 19.3%, and atelectasis 18.3%. Multivariable logistic regression incorporating binary and scored POCUS findings were able to identify those at highest risk for need of mechanical ventilation (area under the curve 0.80) and in-hospital mortality (area under the curve 0.87). In the binary model ultrasound (US) findings in the anterior lung fields were significantly associated with a need for intubation and mechanical ventilation (odds ratio [OR] 3.67; 0.62-21.6). There was an inverse relationship between mortality and posterior lung field involvement (OR 0.05; 0.01-0.23; and scored OR of 0.57; 0.40-0.82). Anterior lung field involvement was not associated with mortality.

CONCLUSIONS

In patients with COVID-19, the anatomic distribution of findings on lung ultrasound is associated with outcomes. Lung POCUS-based models may help clinicians to identify those patients with COVID-19 at risk for clinical deterioration.Key Words: COVID-19; Lung Ultrasound; Mechanical ventilation; Prediction; ICU admission; Mortality; Clinical outcome; Risk stratification; Diagnostic accuracy.

Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease

(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients' prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; p-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; p-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.

Cardiovascular findings on chest computed tomography associated with COVID-19 adverse clinical outcomes

STUDY OBJECTIVE

Chest computed tomography (chest CT) is routinely obtained to assess disease severity in COVID-19. While pulmonary findings are well-described in COVID-19, the implications of cardiovascular findings are less well understood. We evaluated the impact of cardiovascular findings on chest CT on the adverse composite outcome (ACO) of hospitalized COVID-19 patients.

SETTING/PARTICIPANTS

245 COVID-19 patients who underwent chest CT at Rush University Health System were included.

DESIGN

Cardiovascular findings, including coronary artery calcification (CAC), aortic calcification, signs of right ventricular strain [right ventricular to left ventricular diameter ratio, pulmonary artery to aorta diameter ratio, interventricular septal position, and inferior vena cava (IVC) reflux], were measured by trained physicians.

INTERVENTIONS/MAIN OUTCOME MEASURES

These findings, along with pulmonary findings, were analyzed using univariable logistic analysis to determine the risk of ACO defined as intensive care admission, need for non-invasive positive pressure ventilation, intubation, in-hospital and 60-day mortality. Secondary endpoints included individual components of the ACO.

RESULTS

Aortic calcification was independently associated with an increased risk of the ACO (odds ratio 1.86, 95% confidence interval (1.11-3.17) p < 0.05). Aortic calcification, CAC, abnormal septal position, or IVC reflux of contrast were all significantly associated with 60-day mortality and major adverse cardiovascular events. IVC reflux was associated with in-hospital mortality (p = 0.005).

CONCLUSION

Incidental cardiovascular findings on chest CT are clinically important imaging markers in COVID-19. It is important to ascertain and routinely report cardiovascular findings on CT imaging of COVID-19 patients as they have potential to identify high risk patients.

In-hospital prognostic role of coronary atherosclerotic burden in COVID-19 patients

AIMS

Currently, there are few available data regarding a possible role for subclinical atherosclerosis as a risk factor for mortality in Coronavirus Disease 19 (COVID-19) patients. We used coronary artery calcium (CAC) score derived from chest computed tomography (CT) scan to assess the in-hospital prognostic role of CAC in patients affected by COVID-19 pneumonia.

METHODS

Electronic medical records of patients with confirmed diagnosis of COVID-19 were retrospectively reviewed. Patients with known coronary artery disease (CAD) were excluded. A CAC score was calculated for each patient and was used to categorize them into one of four groups: 0, 1-299, 300-999 and at least 1000. The primary endpoint was in-hospital mortality for any cause.

RESULTS

The final population consisted of 282 patients. Fifty-seven patients (20%) died over a follow-up time of 40 days. The presence of CAC was detected in 144 patients (51%). Higher CAC score values were observed in nonsurvivors [median: 87, interquartile range (IQR): 0.0-836] compared with survivors (median: 0, IQR: 0.0-136). The mortality rate in patients with a CAC score of at least 1000 was significantly higher than in patients without coronary calcifications (50 vs. 11%) and CAC score 1-299 (50 vs. 23%), P < 0.05. After adjusting for clinical variables, the presence of any CAC categories was not an independent predictor of mortality; however, a trend for increased risk of mortality was observed in patients with CAC of at least 1000.

CONCLUSION

The correlation between CAC score and COVID-19 is fascinating and under-explored. However, in multivariable analysis, the CAC score did not show an additional value over more robust clinical variables in predicting in-hospital mortality. Only patients with the highest atherosclerotic burden (CAC ≥1000) could represent a high-risk population, similarly to patients with known CAD.

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.

A Novel Multicolour-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases Using Chest X-ray Images

OBJECTIVES

Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause acute respiratory distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures.

METHODS

Nineteen separate-colored layers were created from X ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. On a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected uncleared abnormalities varied a little.

RESULTS

The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infection when compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing the X-rays into five severity levels.

CONCLUSION

By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.

The relationship between lesion density change in chest computed tomography and clinical improvement in COVID-19 patients

OBJECTIVE

To evaluate the association of changes in chest computed tomography (CT) lesion densities with clinical improvement in COVID-19 patients.

METHODS

This was a cross-sectional analysis of hospitalised COVID-19 patients who underwent repeated chest CT. Patients who improved clinically but showed radiological progression were included. Demographic data, presentation complaints and laboratory results were retrieved from the electronic database of the hospital. Lesion density that was measured in Hounsfield units was compred between admission and discharge chest CT scans.

RESULTS

Forty patients (21 males, mean age 47.4 ± 15.1 years) were included in the analysis. The median white blood cell count and C-reactive protein significantly decreased, whereas the median lymphocyte count significantly increased at discharge compared with the admission values. The mean density significantly reduced from admission to discharge.

CONCLUSION

This is the first study in the literature reporting reduction in chest CT lesion densities correlated with clinical and laboratory improvement in COVID-19 patients.

Clinical and laboratory findings of COVID-19: A systematic review and meta-analysis

BACKGROUND/PURPOSE

The aim of this study was to systematically review all COVID-19 publications to summarize the clinical features, assess comorbidities, prevalence, and disease outcomes.

METHODS

Included were all COVID-19 published studies between January 1 to July 20, 2020. The random effect model was used to calculate the pooled prevalence and corresponding 95% confidence interval (CI). Publication bias was assessed using the funnel plot for the standard error by logit event.

RESULTS

The mean age of the patients was 46.8 years (95% CI, 41.0-52.6) and males comprised 54.0% (95% CI, 51.3-56.7). Total co-morbidities prevalence was 29.5% (95% CI, 19.0-36.6), with diabetes mellitus being the most prevalent 13.8% (95% CI, 8.7-21.1), followed by hypertension 11.7% (95% CI, 5.7-22.6), and cardiovascular disease 9.7% (95% CI, 6.5-14.2). The most common clinical manifestations were fever, 82.0% (95% CI, 67.7-90.8), cough 54.3% (95% CI, 45.5-62.9), fatigue 30.2% (95% CI, 23.3-38.1), sputum 28.5% (95% CI, 21.2-37.2), sore throat 21.7% (95% CI, 14.6-31.0), and headache 11.0% (95% CI, 7.9-15.2). The most common COVID-19 serious complications were RNA Anemia 98.2% (95% CI, 96.2-99.2), hospitalization 83.7% (95% CI, 76.0-89.3), bilateral pneumonia 70.9% (95% CI, 58.2-81.0); of those hospitalized 43.5% (95% CI, 24.9-64.2) were discharged. Fatality accounted for 10.5% (95% CI 6.8-16.1).

CONCLUSION

Patients infected with COVID-19 coronavirus showed a wide range of clinical presentation with non-specific symptoms.

An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines.

Correlation between venous blood gas indices and radiological involvements of COVID-19 patients at first admission to emergency department

BACKGROUND

The purpose of this study was to investigate the relation between venous blood gas and chest computerized tomography findings and the clinical conditions of COVID-19 pneumonia.

METHODS

A total of 309 patients admitted to the emergency department and subsequently confirmed COVID-19 cases was examined. Patients with pneumonia symptoms, chest computerized tomography scan, venous blood gas findings, and confirmed COVID-19 on reverse transcription-polymerase chain reaction (PCR) were consecutively enrolled. Multiple linear regression was used to predict computerized tomography and blood gas findings by clinical/laboratory data.

RESULTS

The median age of patients was 51 (interquartile range 39-66), and 51.5% were male. The mortality rate at the end of follow-up was 18.8%. With respect to survival status of patients pCO2 and HCO3 levels and total computerized tomography score values were found to be higher in the surviving patients (p<0.001 and p=0.003, respectively), whereas pH and lactate levels were higher in patients who died (p=0.022 and p=0.001, respectively). With logistic regression analysis, total tomography score was found to be significantly effective on mortality (p<0.001). The diffuse and random involvement of the lungs had a significant effect on mortality (p<0.001, 95%CI 3.853-38.769, OR 12.222 and p=0.027; 95%CI 1.155-11.640, OR 3.667, respectively). With linear regression analysis, the effect of pH and lactate results were found to have a positive effect on total tomography score (p=0.003 and p<0.001, respectively), whereas pCO2 was found to have a negative effect (p=0.029).

CONCLUSION

There was correlation between venous blood gas indices and radiologic scores in COVID-19 patients. Venous blood gas taken in emergency department can be a fast, applicable, minor-invasive, and complementary test in terms of diagnosing COVID-19 pneumonia and predicting the prognosis of disease.

Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT

Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions.

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia

Purpose

Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Methods

The multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >−200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis.

Results

Moderate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission.

Conclusion

DL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Coronary artery calcification and epicardial adipose tissue as independent predictors of mortality in COVID-19

Recent epidemiological studies have demonstrated that common cardiovascular risk factors are strongly associated with adverse outcomes in COVID-19. Coronary artery calcium (CAC) and epicardial fat (EAT) have shown to outperform traditional risk factors in predicting cardiovascular events in the general population. We aim to determine if CAC and EAT determined by Computed Tomographic (CT) scanning can predict all-cause mortality in patients admitted with COVID-19 disease. We performed a retrospective, post-hoc analysis of all patients admitted to Montefiore Medical Center with a confirmed COVID-19 diagnosis from March 1st, 2020 to May 2nd, 2020 who had a non-contrast CT of the chest within 5 years prior to admission. We determined ordinal CAC scores and quantified the epicardial (EAT) and thoracic (TAT) fat volume and examined their relationship with inpatient mortality. A total of 493 patients were analyzed. There were 197 deaths (39.95%). Patients who died during the index admission had higher age (72, [64–80] vs 68, [57–76]; p < 0.001), CAC score (3, [0–6] vs 1, [0–4]; p < 0.001) and EAT (107, [70–152] vs 94, [64–129]; p = 0.023). On a competing risk analysis regression model, CAC ≥ 4 and EAT ≥ median (98 ml) were independent predictors of mortality with increased mortality of 63% (p = 0.003) and 43% (p = 0.032), respectively. As a composite, the group with a combination of CAC ≥ 4 and EAT ≥ 98 ml had the highest mortality. CAC and EAT measured from chest CT are strong independent predictors of inpatient mortality from COVID-19 in this high-risk cohort.

Artificial intelligence-based approaches for COVID-19 patient management

During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.

Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry

PURPOSE

To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry.

METHODS

Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group.

RESULTS

Forty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R²:0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R²:0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64).

CONCLUSIONS

Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.

U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19

The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan-Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10-14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.

Generalized additive mixed model to evaluate the association between total pulmonary infection volume and volume ratio, and clinical types, in patients with COVID-19 pneumonia: a propensity score analysis

Objectives

To investigate the association between longitudinal total pulmonary infection volume and volume ratio over time and clinical types in COVID-19 pneumonia patients.

Methods

This retrospective review included 367 patients with COVID-19 pneumonia. All patients underwent CT examination at baseline and/or one or more follow-up CT. Patients were categorized into two clinical types (moderate and severe groups). The severe patients were matched to the moderate patients via propensity scores (PS). The association between total pulmonary infection volume and volume ratio and clinical types was analyzed using a generalized additive mixed model (GAMM).

Results

Two hundred and seven moderate patients and 160 severe patients were enrolled. The baseline clinical and imaging variables were balanced using PS analysis to avoid patient selection bias. After PS analysis, 172 pairs of moderate patients were allocated to the groups; there was no difference in the clinical and CT characteristics between the two groups (p > 0.05). A total of 332 patients, including 396 CT scans, were assessed. The impact of total pulmonary infection volume and volume ratio with time was significantly affected by clinical types (p for interaction = 0.01 and 0.01, respectively) using GAMM. Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm³ (95% confidence interval [CI]: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group.

Conclusions

Longitudinal total pulmonary infection volume and volume ratio are independently associated with the clinical types of COVID-19 pneumonia.

Key Points

• The impact of total pulmonary infection volume and volume ratio over time was significantly affected by the clinical types (p for interaction = 0.01 and 0.01, respectively) using the GAMM.

• Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm³(95% CI: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group.

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.

Coronary artery calcification in COVID-19 patients: an imaging biomarker for adverse clinical outcomes

BACKGROUND

Recent studies have demonstrated a complex interplay between comorbid cardiovascular disease, COVID-19 pathophysiology, and poor clinical outcomes. Coronary artery calcification (CAC) may therefore aid in risk stratification of COVID-19 patients.

METHODS

Non-contrast chest CT studies on 180 COVID-19 patients ≥ age 21 admitted from March 1, 2020 to April 27, 2020 were retrospectively reviewed by two radiologists to determine CAC scores. Following feature selection, multivariable logistic regression was utilized to evaluate the relationship between CAC scores and patient outcomes.

RESULTS

The presence of any identified CAC was associated with intubation (AOR: 3.6, CI: 1.4-9.6) and mortality (AOR: 3.2, CI: 1.4-7.9). Severe CAC was independently associated with intubation (AOR: 4.0, CI: 1.3-13) and mortality (AOR: 5.1, CI: 1.9-15). A greater CAC score (UOR: 1.2, CI: 1.02-1.3) and number of vessels with calcium (UOR: 1.3, CI: 1.02-1.6) was associated with mortality. Visualized coronary stent or coronary artery bypass graft surgery (CABG) had no statistically significant association with intubation (AOR: 1.9, CI: 0.4-7.7) or death (AOR: 3.4, CI: 1.0-12).

CONCLUSION

COVID-19 patients with any CAC were more likely to require intubation and die than those without CAC. Increasing CAC and number of affected arteries was associated with mortality. Severe CAC was associated with higher intubation risk. Prior CABG or stenting had no association with elevated intubation or death.

An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients

The ongoing COVID-19 pandemic currently involves millions of people worldwide. Radiology plays an important role in the diagnosis and management of patients, and chest computed tomography (CT) is the most widely used imaging modality. An automatic method to characterize the lungs of COVID-19 patients based on individually optimized Hounsfield unit (HU) thresholds was developed and implemented. Lungs were considered as composed of three components—aerated, intermediate, and consolidated. Three methods based on analytic fit (Gaussian) and maximum gradient search (using polynomial and original data fits) were implemented. The methods were applied to a population of 166 patients scanned during the first wave of the pandemic. Preliminarily, the impact of the inter-scanner variability of the HU-density calibration curve was investigated. Results showed that inter-scanner variability was negligible. The median values of individual thresholds th1 (between aerated and intermediate components) were −768, −780, and −798 HU for the three methods, respectively. A significantly lower median value for th2 (between intermediate and consolidated components) was found for the maximum gradient on the data (−34 HU) compared to the other two methods (−114 and −87 HU). The maximum gradient on the data method was applied to quantify the three components in our population—the aerated, intermediate, and consolidation components showed median values of 793 ± 499 cc, 914 ± 291 cc, and 126 ± 111 cc, respectively, while the median value of the first peak was −853 ± 56 HU.

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

Contamination inside CT gantry in the SARS-CoV-2 era

We investigated whether the internal gantry components of our computed tomography (CT) scanner contain severe acute respiratory syndrome 2 (SARS-CoV-2) ribonucleic acid (RNA), bacterial or fungal agents. From 1 to 27 March 2020, we performed 180 examinations of patients with confirmed SARS-CoV-2 infection using a dedicated CT scanner. On 27 March 2020, this CT gantry was opened and sampled in each of the following components: (a) gantry case; (b) inward airflow filter; (c) gantry motor; (d) x-ray tube; (e) outflow fan; (f) fan grid; (g) detectors; and (h) x-ray tube filter. To detect SARS-CoV-2 RNA, samples were analysed using reverse transcriptase-polymerase chain reaction (RT-PCR). To detect bacterial or fungal agents, samples have been collected using “replicate organism detection and counting” contact plates of 24 cm², containing tryptic soy agar, and subsequently cultured. RT-PCR detected SARS-CoV-2 RNA in the inward airflow filter sample. RT-PCR of remaining gantry samples did not reveal the presence of SARS-CoV-2 RNA. Neither bacterial nor fungal agents grew in the agar-based growth medium after the incubation period. Our data showed that SARS-Cov-2 RNA can be found inside the CT gantry only in the inward airflow filter. All remaining CT gantry components were devoid of SARS-CoV-2 RNA.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Role of Chest Radiographs and CT Scans and the Application of Artificial Intelligence in Coronavirus Disease 2019

코로나바이러스감염증-19 (coronavirus disease 2019; 이하 COVID-19)는 전 세계적 대유행 질환으로 인류 보건을 위협하고 있다. 흉부 CT 및 흉부X선사진은 COVID-19의 표준 진단검사인 역전사 중합효소 연쇄반응에 더하여 COVID-19 진단 및 중증도 평가에서 중요한 역할을 하고 있다. 본 종설에서는 흉부 CT 및 흉부X선사진의 COVID-19 폐렴에 대한 현재 역할에 대하여 살펴보고 인공지능을 적용한 대표적 초기 연구들과 저자들의 경험을 소개함으로써 향후 활용가치에 대해 살펴보고자 한다.