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A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

European radiology

10.1007/s00330-020-07044-9 10.1136/bmj.m406 10.1056/NEJMoa2001017 10.1136/bmj.m641 10.1016/j.meegid.2020.104211 10.1007/s10916-020-1536-6 10.1038/s41591-018-0300-7 10.1038/s41586-019-1390-1 10.1101/2020.02.14.20023028v2 10.7150/thno.46465 10.1038/nature14539 10.1148/radiol.2018180237 10.1371/journal.pmed.1002686

Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

The European respiratory journal

10.1183/13993003.01104-2020 10.1001/jama.2020.3204 10.1056/NEJMoa2002032 10.1097/CCM.0000000000004411 10.1056/NEJM199701233360402 10.1056/NEJMc1906060 10.1038/nrclinonc.2017.141 10.1016/S0140-6736(20)30260-9 10.1373/49.1.1 10.1164/rccm.201908-1581ST 10.18637/jss.v036.i11 10.2307/2531595 10.1016/S0140-6736(20)30183-5 10.1001/jama.2020.1585 10.1038/s41586-020-2012-7 10.1016/S2213-2600(20)30116-8 10.1056/NEJMoa2001282 10.1016/S2213-2600(20)30076-X 10.1148/radiol.2020200274 10.1148/RADIOL.2020200843 10.1148/radiol.2020200230 10.1016/S1473-3099(20)30086-4 10.1136/thorax.58.8.686 10.1016/S0140-6736(19)33221-0 10.1200/CCI.19.00047 10.1016/j.radonc.2019.11.019

Chest CT Evaluation of 11 Persistent Asymptomatic Patients with SARS-CoV-2 Infection.

In total, 11 asymptomatic carriers who underwent nasal or oropharyngeal swab tests for SARS-CoV-2 after being in close contact with patients who developed symptomatic 2019 coronavirus disease (COVID-19) were enrolled in this study. The chest multidetector computed tomography (CT) images of the enrolled patients were qualitatively and quantitatively analyzed. The findings of the first chest CT were normal in 3 (27.3%) patients, 2 of whom were aged below 15 years. The lesions of 2 (18.2%) patients involved 1 lobe with unifocal presence. Subpleural lesions were observed in 7 (63.6%) patients. Ground glass opacity (GGO) was the most common sign observed in 7 (63.6%) patients. Crazy-paving pattern and consolidation were detected in 2 (18.2%) and 4 (36.4%) patients, respectively. Based on deep learning and quantitative analysis, the mean volume of intrapulmonary lesions in the first CT image was 85.73 ± 84.46 cm

Japanese journal of infectious diseases

10.7883/yoken.JJID.2020.264

Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.

Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.

Studies in health technology and informatics

10.3233/SHTI200481

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.

Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays. In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2020.105608

Truncated inception net: COVID-19 outbreak screening using chest X-rays.

Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID-19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis, and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using the acquired CXRs, and proves the viability of using the proposed Truncated Inception Net as a screening tool.

Physical and engineering sciences in medicine

10.1007/s13246-020-00888-x 10.1016/j.acra.2020.03.003 10.1148/radiol.2020200432 10.1016/S0140-6736(20)30183-5 10.1007/s10916-020-01562-1 10.1007/s11548-016-1359-6 10.1007/s11548-015-1242-x 10.1007/s10916-018-0991-9 10.1109/TMI.2017.2775636

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non-COVID-19 pneumonia and nonpneumonia diseases. A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Journal of medical Internet research

10.2196/19569 10.1056/NEJMoa2004500 10.1080/22221751.2020.1745095 10.1080/22221751.2020.1745095 10.1002/jmv.25786 10.1148/radiol.2020200642 10.1148/radiol.2020201365 10.3390/diagnostics10040202 10.3348/kjr.2020.0146 10.1016/j.ejrad.2020.108961 10.1148/radiol.2020201326 10.1148/radiol.2020200823 10.1148/radiol.2020200905 10.1007/s003300101126 10.1016/j.ejrad.2017.01.016 10.1109/CVPR.2016.90 10.1109/cvpr.2016.308 10.1109/cvpr.2017.195 10.1162/neco_a_00990 10.1109/iccv.2017.74 10.1148/radiol.2020200905 10.1101/2020.03.12.20027185 10.1109/jtehm.2018.2837901 10.1109/cvpr.2009.5206848 10.1109/iros.2015.7353481 10.3348/kjr.2020.0132 10.1148/radiol.2020200988 10.3348/kjr.2019.0025 10.1007/s10916-020-01562-1

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.

Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

Computers in biology and medicine

10.1016/j.compbiomed.2020.103805 10.1056/nejmc2001468 10.1016/S0140-6736(20)30522-5 10.1136/bmj.m800 10.1038/s41368-020-0075-9 10.1016/j.mehy.2019.109503 10.1007/s10462-018-9641-3 10.1016/j.measurement.2019.05.076 10.1038/s41598-019-42294-8 10.1186/s40537-019-0276-2 10.1155/2018/4168538 10.1155/2019/4180949 10.3390/app10020559 10.2214/AJR.20.22969 10.1109/cvpr.2018.00474 10.1016/j.mehy.2019.109531 10.3390/ijgi8120582 10.1016/j.optlaseng.2019.05.005 10.3390/s19050982 10.3906/elk-1801-157 10.3390/rs12010120 10.1007/s41745-019-0098-4 10.1007/978-1-4302-5990-9_3 10.2339/politeknik.369132 10.3390/app10010243 10.1016/j.fss.2019.09.013 10.13140/2.1.3014.6562 10.1016/j.ejfs.2016.06.001 10.1016/j.eswa.2019.05.035 10.1016/j.bbe.2019.11.001

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

Computers in biology and medicine

10.1016/j.compbiomed.2020.103795 10.1148/radiol.2020200527 10.1148/radiol.2020200823 10.1016/S0140-6736(20)30673-5

Automated detection of COVID-19 cases using deep neural networks with X-ray images.

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

Computers in biology and medicine

10.1016/j.compbiomed.2020.103792 10.1148/radiol.2020200490 10.1148/radiol.2020200527 10.1148/radiol.2020200343 10.1148/radiol.2020200463 10.1148/radiol.2020200370 10.1038/nature21056 10.1101/2020.03.12.20027185 10.1148/radiol.2020200823

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.

In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time. From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years). Patient CT images from one hospital were divided among training, validation and test datasets with an 80%:10%:10% ratio. An end-to-end representation learning method using a large-scale bi-directional generative adversarial network (BigBiGAN) architecture was designed to extract semantic features from the CT images. The semantic feature matrix was input for linear classifier construction. Patients from the other hospital were used for external validation. Differentiation accuracy was evaluated using a receiver operating characteristic curve. Based on the 120-dimensional semantic features extracted by BigBiGAN from each image, the linear classifier results indicated that the area under the curve (AUC) in the training, validation and test datasets were 0.979, 0.968 and 0.972, respectively, with an average sensitivity of 92% and specificity of 91%. The AUC for external validation was 0.850, with a sensitivity of 80% and specificity of 75%. Publicly available architecture and computing resources were used throughout the study to ensure reproducibility. This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.

European journal of nuclear medicine and molecular imaging

10.1007/s00259-020-04929-1 10.1016/S0140-6736(20)30323-8 10.1007/s00259-020-04735-9 10.1093/cid/ciu053 10.1016/j.ejrad.2020.108991

Optical techniques, computed tomography and deep learning role in the diagnosis of COVID-19 pandemic towards increasing the survival rate of vulnerable populations.

• Severe lung complications can be explored using computed tomography during COVID-19 pandemic. • Ultra-low dose CT can enhance COVID-19 infected patients diagnostic capability. • Optically monitored CT along with deep learning is the best solution for diagnosis of COVID-19 during pandemic. • CT scans sensitivity (88 %) is preferable on clinical approach sensitivity (59 %) for COVID-19 suspected patients. • CT and Computer aided approaches helps the radiologist to make fast and accurate diagnosis during COVID-19 pandemic.

Photodiagnosis and photodynamic therapy

10.1016/j.pdpdt.2020.101880 10.1007/s00330-020-06801-0 10.1007/s00330-020-06731-x 10.1016/j.pdpdt.2020.101836 10.1016/j.pdpdt.2020.101823 10.1016/j.ijantimicag.2020.105954 10.1097/rli.0000000000000670 10.21037/qims.2018.06.05 10.1080/14737159.2020.1766968 10.1007/s10489-020-01714-3 10.1101/2020.02.14.20023028 10.1109/tip.2011.2107328 10.1055/a-1154-8795 10.1136/bmj.m641 10.2214/ajr.20.22954

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several

Chaos, solitons, and fractals

10.1016/j.chaos.2020.109944 10.22207/JPAM.14.SPL1.40 10.21203/rs.3.rs-26500/v1 10.1016/j.chaos.2020.109864 10.1007/s00521-018-3381-9 10.1186/s40708-020-00105-1

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays. In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases. CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available. CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2020.105581 10.1136/bmj.m641 10.1148/radiol.2020200432 10.1148/radiol.2020200343

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.

In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

Physical and engineering sciences in medicine

10.1007/s13246-020-00865-4 10.1016/S2213-2600(20)30076-X 10.1109/TMI.2016.2553401 10.1561/2000000039 10.1016/j.cell.2018.02.010 10.1186/s40537-016-0043-6 10.1021/ci0342472

Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China.

To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups. A total of 484 patients (median age of 47 years, interquartile range 33-57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13-15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients. Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient's condition during the course of illness. • Volume, density, and location of the pulmonary opacity on CT change over time in COVID-19. • The evolution of CT appearance follows specific pattern, varying with disease severity.

European radiology

10.1007/s00330-020-06976-6 10.1016/S1473-3099(20)30086-4 10.1016/S0140-6736(20)30183-5 10.1016/S0140-6736(10)61459-6 10.1148/rg.242035193 10.1002/jmv.25709

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection which is not good enough. Some works have been provided for all classes. However, the results suffer due to lack of data for rare classes and data unbalancing problem. Due to the rise in COVID-19 cases, medical facilities in many countries are overwhelmed and there is a need for an intelligent system to detect it. Few works have been done regarding the detection of the coronavirus but there are many cases where it can be misclassified as some techniques are not efficient and can only identify specific diseases. This work is a deep learning- based model to distinguish COVID-19 cases from other chest diseases. A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides an effective analysis of chest-related diseases taking into account both age and gender. Our model achieves 87% accuracy in terms of GAN-based synthetic data and presents four different types of deep learning-based models that provide comparable results to other state-of-the-art techniques. The healthcare industry may face unfavorable consequences if the gap in the identification of all types of pneumonia is not filled with effective automation.

Current medical imaging

10.2174/1573405616666200604163954

Artificial Intelligence: Promise, Pitfalls, and Perspective.

JAMA

10.1001/jama.2020.8737

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios.

The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. Although CT scan is the gold standard, CXR are still useful because it is cheaper, faster and more widespread. This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. In order to achieve the objectives, we have proposed a classification schema considering the following perspectives: i) a multi-class classification; ii) hierarchical classification, since pneumonia can be structured as a hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in the schema in order to re-balance the classes distribution. We observed that, texture is one of the main visual attributes of CXR images, our classification schema extract features using some well-known texture descriptors and also using a pre-trained CNN model. We also explored early and late fusion techniques in the schema in order to leverage the strength of multiple texture descriptors and base classifiers at once. To evaluate the approach, we composed a database, named RYDLS-20, containing CXR images of pneumonia caused by different pathogens as well as CXR images of healthy lungs. The classes distribution follows a real-world scenario in which some pathogens are more common than others. The proposed approach tested in RYDLS-20 achieved a macro-avg F1-Score of 0.65 using a multi-class approach and a F1-Score of 0.89 for the COVID-19 identification in the hierarchical classification scenario. As far as we know, the top identification rate obtained in this paper is the best nominal rate obtained for COVID-19 identification in an unbalanced environment with more than three classes. We must also highlight the novel proposed hierarchical classification approach for this task, which considers the types of pneumonia caused by the different pathogens and lead us to the best COVID-19 recognition rate obtained here.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2020.105532 10.1007/s11263-009-0315-0 10.1016/j.patcog.2013.11.029 10.1016/j.eswa.2018.01.038

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

The European respiratory journal

10.1183/13993003.00775-2020 10.1016/S2213-2600(20)30079-5 10.1016/S2214-109X(20)30068-1 10.1183/13993003.00334-2020 10.1016/S1473-3099(20)30134-1 10.1183/13993003.00986-2018 10.1016/S2213-2600(18)30286-8 10.1016/S2213-2600(20)30003-5 10.1183/13993003.01216-2019 10.1016/j.media.2017.06.014 10.1101/2020.02.14.20023028 10.1016/j.media.2014.07.003 10.1016/j.radonc.2018.10.019 10.1101/2020.03.19.20039354 10.1101/2020.02.23.20026930 10.1101/2020.03.20.20039834 10.1148/radiol.2020200905 10.1101/2020.03.12.20027185

Generalizability of Deep Learning Tuberculosis Classifier to COVID-19 Chest Radiographs: New Tricks for an Old Algorithm?

Journal of thoracic imaging

10.1097/RTI.0000000000000532

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Cell

10.1016/j.cell.2020.04.045 10.1109/ICPR.2018.8546325 10.1016/s2213-2600(20)30079-5

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases.

While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks. Training the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.

Journal of medical and biological engineering

10.1007/s40846-020-00529-4 10.1007/s13246-020-00865 10.1109/TMI.2016.2553401 10.1631/FITEE.1700808 10.1016/j.cell.2018.02.010 10.1109/TMI.2016.2528162 10.1109/TMI.2018.2791721 10.3389/fnins.2018.00804 10.1164/rccm.201802-0350LE 10.1097/COH.0b013e32833ed177 10.1002/mp.12820 10.2214/AJR.16.17224 10.1111/tmi.13383

Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.

To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively. Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.

European journal of radiology

10.1016/j.ejrad.2020.109041 10.1016/S0140-6736(20)30154-9 10.1016/j.ejrad.2020.108961 10.1016/S1473-3099(20)30086-4 10.1148/radiol.2020200642 10.1109/TMI.2018.2876510 10.1001/jamanetworkopen.2019.2561 10.1109/TMI.2017.2759102 10.1038/s41591-018-0177-5 10.1183/13993003.00986-2018 10.1016/j.tranon.2017.08.007 10.1109/Cvpr.2016.90 10.1007/s00259-020-04735-9 10.1056/NEJMoa2002032 10.1007/s11432-020-2849-3 10.1148/radiol.2020200905 10.1148/radiol.2020200241 10.1148/radiol.2020200823 10.1093/cid/ciaa247

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound.

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

IEEE transactions on medical imaging

10.1109/TMI.2020.2994459

Using X-ray images and deep learning for automated detection of coronavirus disease.

Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.

Journal of biomolecular structure & dynamics

10.1080/07391102.2020.1767212 10.1080/07391102.2020.1758790 10.1080/07391102.2020.1763199 10.1016/j.cmpb.2018.01.017 10.1080/07391102.2020.1754293 10.1016/j.patrec.2019.11.013 10.1184/R1/6606860.v1 10.1080/07391102.2020.1758788 10.1016/j.pdisas.2020.100091 10.1016/j.onehlt.2020.100124 10.1080/07391102.2020.1761882 10.1080/07391102.2020.1761881 10.1080/07391102.2020.1758789 10.1016/j.ajem.2020.04.003 10.1080/07391102.2020.1758791 10.1080/07391102.2020.1756411 10.1080/07391102.2020.1760136 10.1016/j.jpainsymman.2020.03.025 10.1016/j.dsx.2020.04.001 10.1109/CVPR.2016.90 10.1109/CVPR.2017.243 10.1148/radiol.2020200330 10.1016/S0140-6736(20)30553-5 10.17632/rscbjbr9sj.2 10.1016/j.physio.2020.03.003 10.1016/j.puhe.2020.03.027 10.1016/j.ophtha.2020.03.037 10.1056/NEJMoa2001316 10.1016/j.jinf.2020.03.007 10.1016/j.cca.2020.03.009 10.1016/S0140-6736(20)30313-5 10.1080/07391102.2020.1751300 10.1080/07391102.2020.1752802 10.1148/ryct.2020200034 10.1080/07391102.2020.1757510 10.1080/07391102.2020.1753580 10.1080/07391102.2020.1761883 10.1080/07391102.2020.1760137 10.1080/07391102.2020.1753577 10.1007/978-3-319-24574-4_28 10.1016/j.idm.2020.02.002 10.1016/j.jaut.2020.102433 10.1016/j.scitotenv.2020.138532 10.1016/j.beproc.2018.01.004 10.1080/07391102.2020.1751298 10.1109/CVPR.2018.00474 10.1080/07391102.2020.1762741 10.1016/j.nmni.2020.100669 10.1016/j.ctro.2020.03.009 10.1109/WACV.2017.58 10.1080/07391102.2020.1763201 10.1016/j.molmed.2020.02.008 10.1080/07391102.2020.1763202 10.1080/07391102.2020.1762743 10.1016/S1473-3099(20)30129-8 10.1016/j.ijid.2020.03.017 10.1016/j.promfg.2020.01.375

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

IEEE transactions on medical imaging

10.1109/TMI.2020.2993291

Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients.

Theranostics

10.7150/thno.46465

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images.

The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.

Medical hypotheses

10.1016/j.mehy.2020.109761 10.1016/j.ijantimicag.2020.105924 10.1001/jama.2020.1585 10.1016/j.cca.2020.03.009 10.1093/clinchem/hvaa029 10.1016/j.cell.2018.02.010 10.1148/radiol.2017162326 10.1016/j.mehy.2019.109426 10.1016/j.mehy.2019.109433 10.1109/ACCESS.2020.2982017 10.11989/JEST.1674-862X.80904120 10.1016/j.patcog.2017.10.013 10.1016/j.neunet.2020.01.017 10.1016/j.bbe.2020.01.010 10.1007/s00500-019-04355-y 10.1109/TII.2019.2907373 10.1016/j.catena.2019.104249 10.1016/s1474-6670(17)67769-3 10.1109/JPROC.2015.2494218 10.1016/j.neunet.2018.07.011

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology

10.1007/s10096-020-03901-z

Coronavirus Disease 2019 Deep Learning Models: Methodologic Considerations.

Radiology

10.1148/radiol.2020201178 10.1148/radiol.2020200905

Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak.

Journal of thoracic imaging

10.1097/RTI.0000000000000512

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively;

Radiology

10.1148/radiol.2020200905 10.1148/radiol.2020200642 10.1148/radiol.2020200432

False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases.

The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negative results of real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Here, we report two representative false-negative cases and discuss the supplementary role of clinical data with rRT-PCR, including laboratory examination results and computed tomography features. Coinfection with SARS-COV-2 and other viruses has been discussed as well.

Korean journal of radiology

10.3348/kjr.2020.0146 10.1101/2020.02.07.937862 10.1148/radiol.2020200343 10.1148/radiol.2020200230

Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.

Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological parameters simultaneously. In this work, we introduce an optimization scheme to increase the sensitivity of the ASL fingerprint. We also propose a regression based estimation framework for MRF-ASL. To improve the sensitivity of MRF-ASL signals to underlying parameters, we optimized ASL labeling durations using the Cramer-Rao Lower Bound (CRLB). This paper also proposes a neural network regression based estimation framework trained using noisy synthetic signals generated from our ASL signal model. We tested our methods in silico and in vivo, and compared with multiple post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL. We present comparisons of estimated maps for the six parameters of our signal model. The scan design process facilitated precise estimates of multiple hemodynamic parameters and tissue properties from a single scan, in regions of normal gray and white matter, as well as regions with anomalous perfusion activity in the brain. In particular, there was a 86.7% correlation of perfusion estimates with the ground truth in silico, using our proposed techniques. In vivo, there was roughly a 7 fold improvement in the Coefficient of Variation (CoV) for white matter perfusion, and 2 fold improvement in gray matter perfusion CoV in comparison to a reference Multi PLD method. The regression based estimation approach provided perfusion estimates rapidly, with estimation times of around 1s per map. Scan design optimization, coupled with regression-based estimation is a powerful tool for improving precision in MRF-ASL.

Magnetic resonance in medicine

10.1002/mrm.28051