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in category radiology and imaging

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1: Correlation between Chest CT Severity Scores and the Clinical Parameters of Adult Patients with COVID-19 pneumonia
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Posted 20 Oct 2020

Correlation between Chest CT Severity Scores and the Clinical Parameters of Adult Patients with COVID-19 pneumonia
33,471 downloads medRxiv radiology and imaging

Ghufran Aref Saeed, Waqar Gaba, Asad Shah, Abeer Ahmed Al Helali, Emadullah Raidullah, Ameirah Bader Al Ali, Mohammed Elghazali, Deena Yousef Ahmed, Shaikha Ghanam Al Kaabi, Safaa Almazrouei

PurposeOur aim is to correlate the clinical condition of patients with COVID-19 infection with the 25 Point CT severity score by Chang et al (devised for assessment of ARDS in patients with SARS in 2005). Material and MethodsData of consecutive symptomatic patients who were suspected to have COVID-19 infection and presented to our hospital, was collected from March to April 2020. All patients underwent two consecutive RT-PCR tests and had a non-contrast HRCT scan done at presentation. From the original cohort of 1062 patients, 160 patients were excluded leaving a total number of 902 patients. ResultsThe mean age was 44.2 {+/-}11.9 years [85.3%males, 14.7%females]. CT severity score found to be positively correlated with lymphopenia, increased serum CRP, d-dimer and ferritin levels (p < 0.0001). The oxygen requirements as well as length of hospital stay were increasing with the increase of scan severity. ConclusionThe 25-point CT severity score correlates well with the COVID-19 clinical severity. Our data suggest that chest CT scoring system can aid in predicting COVID-19 disease outcome and significantly correlates with lab tests and oxygen requirements.

2: Diagnostic power of chest CT for COVID-19: to screen or not to screen
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Posted 22 May 2020

Diagnostic power of chest CT for COVID-19: to screen or not to screen
15,497 downloads medRxiv radiology and imaging

Kristof De Smet, Dieter De Smet, Ingel Demedts, Bernard Bouckaert, Thomas Ryckaert, Emanuel Laridon, Birgit Heremans, Ruben Vandenbulcke, Stefaan Gryspeerdt, Geert Antoine Martens

Background: chest CT is increasingly used for COVID-19 screening in healthcare systems with limited SARS-CoV-2 PCR capacity. Its diagnostic value was supported by studies with methodological concerns and its use is controversial. Here we investigated its potential to diagnose COVID-19 in symptomatic patients and to screen asymptomatic patients in a prospective study with minimal selection bias. Methods: From March 19, 2020 to April 20, 2020 we performed parallel SARS-CoV-2 PCR and CT with categorization of COVID-19 suspicion by CO-RADS, in 859 patients with COVID-19 symptoms and 1138 controls admitted to the hospital for COVID-19 unrelated medical urgencies. CT-CORADS was categorized on a 5-point scale from 1 (very low suspicion) to 5 (very high suspicion). AUC under ROC curve were calculated in symptomatic versus asymptomatic patients to predict positive SARS-CoV-2 positive PCR and likelihood ratios for each CO-RADS score were used for rational selection of diagnostic thresholds. Findings: CT-CORADS had significant (P<0.0001) diagnostic power in both symptomatic (AUC=0.891) and asymptomatic (AUC=0.700) patients hospitalized during SARS-CoV-2 peak prevalence. In symptomatic patients (41.7% PCR+), CO-RADS [&ge;] 3 detected positive PCR with high sensitivity (89.1%) and 72.5% specificity. In asymptomatic patients (5.3% PCR+), a CO-RADS score [&ge;] 3 detected SARS-CoV-2 infection with low sensitivity (45.0%) but high specificity (88.8%). Interpretation: CT-CORADS has meaningful diagnostic power in symptomatic patients, supporting its application for time-sensitive triage. Sensitivity in asymptomatic patients is insufficient to justify its use as screening approach. Incidental detection of CO-RADS [&ge;] 3 in asymptomatic patients should trigger reflex testing for respiratory pathogens.

3: Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
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Posted 25 Feb 2020

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
7,711 downloads medRxiv radiology and imaging

Song Ying, Shuangjia Zheng, Liang Li, Xiang Zhang, Xiaodong Zhang, Ziwang Huang, Jianwen Chen, Huiying Zhao, Ruixuan Wang, Yutian Chong, Jun Shen, Yunfei Zha, Yuedong Yang

BackgroundA novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and MethodsWe collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. ResultsThe experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. ConclusionsThe established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

4: Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning
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Posted 06 May 2020

Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning
7,612 downloads medRxiv radiology and imaging

Sohaib Asif, Yi Wenhui, Hou Jin, Yi Tao, Si Jinhai

The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID-19 pneumonia patients using digital chest x-ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID-19, 1345 viral pneumonia and 1341 normal chest x-ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.

5: Covid-19 Detection using CNN Transfer Learning from X-ray Images
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Posted 18 May 2020

Covid-19 Detection using CNN Transfer Learning from X-ray Images
6,175 downloads medRxiv radiology and imaging

Taban Majeed, Rasber Rashid, Dashti Ali, Aras Asaad

The Covid-19 first occurs in Wuhan, China in December 2019. After that the virus spread all around the world and at the time of writing this paper the total number of confirmed cases are above 4.7 million with over 315000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To achieve this task, we first use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images fed into CNN models without any preprocessing to follow the many of researches using chest X-rays in this manner. Next, a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed most to the decision of CNNs back to the original image to visualize the most discriminating regions on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect, and approve, the region(s) of the input image used by CNNs that lead to its prediction.

6: OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease
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Posted 15 Dec 2019

OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease
5,830 downloads medRxiv radiology and imaging

Pamela J. LaMontagne, Tammie Benzinger, John C Morris, Sarah Keefe, Russ Hornbeck, Chengjie Xiong, Elizabeth Grant, Jason Hassenstab, Krista Moulder, Andrei G. Vlassenko, Marcus E. Raichle, Carlos Cruchaga, Daniel Marcus

OASIS-3 is a compilation of MRI and PET imaging and related clinical data for 1098 participants who were collected across several ongoing studies in the Washington University Knight Alzheimer Disease Research Center over the course of 15 years. Participants include 605 cognitively normal adults and 493 individuals at various stages of cognitive decline ranging in age from 42 to 95 years. The OASIS-3 dataset contains over 2000 MR sessions, including multiple structural and functional sequences. PET metabolic and amyloid imaging includes over 1500 raw imaging scans and the accompanying post-processed files from the PET Unified Pipeline (PUP) are also available in OASIS-3. OASIS-3 also contains post-processed imaging data such as volumetric segmentations and PET analyses. Imaging data is accompanied by dementia and APOE status and longitudinal clinical and cognitive outcomes. OASIS-3 is available as an open access data set to the scientific community to answer questions related to healthy aging and dementia.

7: Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
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Posted 17 Apr 2020

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
5,756 downloads medRxiv radiology and imaging

Rahul Kumar, Ridhi Arora, Vipul Bansal, Vinodh J Sahayasheela, Himanshu Buckchash, Javed Imran, Narayanan Narayanan, Ganesh N Pandian, Balasubramanian Raman

According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

8: Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning
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Posted 24 Jun 2020

Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning
5,278 downloads medRxiv radiology and imaging

Rima Arnaout, Lara Curran, Yili Zhao, Jami Levine, Erin Chinn, Anita Moon-Grady

Congenital heart disease (CHD) is the most common birth defect. Fetal survey ultrasound is recommended worldwide, including five views of the heart that together could detect 90% of complex CHD. In practice, however, sensitivity is as low as 30%. We hypothesized poor detection results from challenges in acquiring and interpreting diagnostic-quality cardiac views, and that deep learning could improve complex CHD detection. Using 107,823 images from 1,326 retrospective echocardiograms and surveys from 18-24 week fetuses, we trained an ensemble of neural networks to (i) identify recommended cardiac views and (ii) distinguish between normal hearts and complex CHD. Finally, (iii) we used segmentation models to calculate standard fetal cardiothoracic measurements. In a test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images, about 400 times the size of the training dataset) the model achieved an AUC of 0.99, 95% sensitivity (95%CI, 84-99), 96% specificity (95%CI, 95-97), and 100% NPV in distinguishing normal from abnormal hearts. Sensitivity was comparable to clinicians' task-for-task and remained robust on external and lower-quality images. The model's decisions were based on clinically relevant features. Cardiac measurements correlated with reported measures for normal and abnormal hearts. Applied to guidelines-recommended imaging, ensemble learning models could significantly improve detection of fetal CHD and expand telehealth options for prenatal care at a time when the COVID-19 pandemic has further limited patient access to trained providers. This is the first use of deep learning to approximately double standard clinical performance on a critical and global diagnostic challenge.

9: Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review
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Posted 11 Jul 2020

Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review
5,156 downloads medRxiv radiology and imaging

Wasif Khan, Nazar Zaki, Luqman Ali

Chest radiography is an important diagnostic tool for chest-related diseases. Medical imaging research is currently embracing the automatic detection techniques used in computer vision. Over the past decade, Deep Learning techniques have shown an enormous breakthrough in the field of medical diagnostics. Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review that would assist practitioners in selecting the best methods from a real-time perspective, perceiving the available datasets, and understanding the currently achieved results in this domain. This paper overviews the current literature on pneumonia identification from chest x-ray images. After summarizing the topic, the review analyzes the usability, goodness factors, and computational complexities of the algorithms that implement these techniques. It also discusses the quality, usability, and size of the available datasets, and ways of coping with unbalanced datasets.

10: Development and Evaluation of an AI System for COVID-19
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Posted 23 Mar 2020

Development and Evaluation of an AI System for COVID-19
3,905 downloads medRxiv radiology and imaging

Cheng Jin, Weixiang Chen, Yukun Cao, Zhanwei Xu, Zimeng Tan, Xin Zhang, Lei Deng, Chuansheng Zheng, Jie Zhou, Heshui Shi, Jianjiang Feng

Early detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 970 CT volumes of 496 patients with confirmed COVID-19 and 260 negative cases from three hospitals in Wuhan, China, and 1,125 negative cases from two publicly available chest CT datasets. Trained using only 312 cases, our diagnosis system, which is based on deep convolutional neural network, is able to achieve an accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%, a sensitivity of 94.06%, and a specificity of 95.47% on an independent external verification dataset of 1,255 cases. In a reader study involving five radiologists, only one radiologist is slightly more accurate than the AI system. The AI system is two orders of magnitude faster than radiologists and the code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

11: AI for radiographic COVID-19 detection selects shortcuts over signal
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Posted 14 Sep 2020

AI for radiographic COVID-19 detection selects shortcuts over signal
3,806 downloads medRxiv radiology and imaging

Alex J. DeGrave, Joseph D Janizek, Su-In Lee

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious "shortcuts." Because this approach to data collection has also been used to obtain training data for detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, since the undesired "shortcuts" learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of ML healthcare models.

12: Distinguishing L and H phenotypes of COVID-19 using a single x-ray image
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Posted 03 May 2020

Distinguishing L and H phenotypes of COVID-19 using a single x-ray image
3,430 downloads medRxiv radiology and imaging

Mohammad Tariqul Islam, Jason W. Fleischer

Recent observations have shown that there are two types of COVID-19 response: an H phenotype with high lung elastance and weight, and an L phenotype with low measures. H-type patients have pneumonia-like thickening of the lungs and require ventilation to survive; L-type patients have clearer lungs that may be injured by mechanical assistance. As treatment protocols differ between the two types, and the number of ventilators is limited, it is vital to classify patients appropriately. To date, the only way to confirm phenotypes is through high-resolution computed tomography. Here, we identify L- and H-type patients from their frontal chest x-rays using feature-embedded machine learning. We then apply the categorization to multiple images from the same patient, extending it to detect and monitor disease progression and recovery. The results give an immediate criterion for coronavirus triage and provide a methodology for respiratory diseases beyond COVID-19.

13: Chest X-ray classification using Deep learning for automated COVID-19 screening
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Posted 23 Jun 2020

Chest X-ray classification using Deep learning for automated COVID-19 screening
3,189 downloads medRxiv radiology and imaging

Ankita Shelke, Madhura Inamdar, Vruddhi Shah, Amanshu Tiwari, Aafiya Hussain, Talha Chafekar, Ninad Mehendale

In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

14: Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images
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Posted 16 Dec 2020

Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images
3,131 downloads medRxiv radiology and imaging

Iason Katsamenis, Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

15: Neuroimaging biomarkers differentiate Parkinson disease with and without cognitive impairment and dementia
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Posted 16 Jul 2019

Neuroimaging biomarkers differentiate Parkinson disease with and without cognitive impairment and dementia
2,971 downloads medRxiv radiology and imaging

Conor Owens-Walton, David Jakabek, Brian D. Power, Mark Walterfang, Sara Hall, Danielle van Westen, Jeffrey C.L. Looi, Marnie Shaw, Oskar Hansson

Mild cognitive impairment in Parkinson disease places a high burden on patients and is likely a precursor to Parkinson disease-related dementia. Studying the functional connectivity and morphology of subcortical structures within basal ganglia-thalamocortical circuits may uncover neuroimaging biomarkers of cognitive dysfunction in PD. We used an atlas-based seed region-of-interest approach to investigate resting-state functional connectivity of important subdivisions of the caudate nucleus, putamen and thalamus, between controls (n = 33), cognitively unimpaired Parkinson disease subjects (n = 33), Parkinson disease subjects with mild cognitive impairment (n = 22) and Parkinson disease subjects with dementia (n = 17). We then investigated how the morphology of the caudate, putamen and thalamus structures and differed between groups. Results indicate that cognitively unimpaired Parkinson disease subjects, compared to controls, display increased functional connectivity of the dorsal caudate, anterior putamen and mediodorsal thalamic subdivisions with areas across the frontal lobe, as well as reduced functional connectivity of the dorsal caudate with posterior cortical and cerebellar regions. Compared to controls, Parkinson disease subjects with mild cognitive impairment demonstrated reduced functional connectivity of the mediodorsal thalamus with midline nodes within the executive-control network. Compared to subjects with mild cognitive impairment, subjects with dementia demonstrated reduced functional connectivity of the mediodorsal thalamus with the posterior cingulate cortex, a key node within the default-mode network. Extensive volumetric and surface-based contraction was found in Parkinson disease subjects with dementia. Our research demonstrates how functional connectivity of the caudate, putamen and thalamus are implicated in the pathophysiology of cognitive impairment and dementia in Parkinson disease, with mild cognitive impairment and dementia in Parkinson disease associated with a breakdown in functional connectivity of the mediodorsal thalamus with para- and posterior cingulate regions of the brain.

16: A Systematic Meta-Analysis of CT Features of COVID-19: Lessons from Radiology
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Posted 07 Apr 2020

A Systematic Meta-Analysis of CT Features of COVID-19: Lessons from Radiology
2,951 downloads medRxiv radiology and imaging

Vasantha Kumar Venugopal, Vidur Mahajan, Sriram Rajan, Vikash Agarwal, Ruchika Rajan, Salsabeel Syed, Harsh Mahajan

1.Several studies have been published in the past few months describing the CT features of Coronavirus Disease 2019 (COVID-19). There is a great degree of heterogeneity in the study designs, lesion descriptors used and conclusions derived. In our systematic analysis and meta-review, we have attempted to homogenize the reported features and provide a comprehensive view of the disease pattern and progression in different clinical stages. After an extensive literature search, we short-listed and reviewed 49 studies including over 4145 patients with 3615 RT-PCR positive cases of COVID-19 disease. We have found that there is a good agreement among these studies that diffuse bilateral ground-glass opacities (GGOs) is the most common finding at all stages of the disease followed by consolidations and mixed density lesions. 78% of patients with RT-PCR confirmed COVID-19 infections had either ground-glass opacities, consolidation or both. Inter-lobular septal thickening was also found to be a common feature in many patients in advanced stages. The progression of these initial patchy GGOs and consolidations to diffuse lesions with septal thickening, air bronchograms in the advanced stages, to either diffuse "white-out" lungs needing ICU admissions or finally resolving completely without or with residual fibrotic strips was also found to be congruent among multiple studies. Prominent juxta- lesional pulmonary vessels, pleural effusion and lymphadenopathy in RT-PCR proven cases were found to have poor clinical prognosis. Additionally, we noted wide variation in terminology used to describe lesions across studies and suggest the use of standardized lexicons to describe findings related to diseases of vital importance.

17: A Fully Automated Deep Learning-based Network ForDetecting COVID-19 from a New And Large Lung CT ScanDataset
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Posted 12 Jun 2020

A Fully Automated Deep Learning-based Network ForDetecting COVID-19 from a New And Large Lung CT ScanDataset
2,866 downloads medRxiv radiology and imaging

Mohammad Rahimzadeh, Abolfazl Attar, Seyed Mohammad Sakhaei

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

18: Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans
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Posted 27 Apr 2020

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans
2,807 downloads medRxiv radiology and imaging

Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

19: A globally available COVID-19 - Template for clinical imaging studies
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Posted 07 Apr 2020

A globally available COVID-19 - Template for clinical imaging studies
2,359 downloads medRxiv radiology and imaging

Gabriel Alexander Salg, M.K. Ganten, M. Baumhauer, CP Heu├čel, J. Kleesiek

BackgroundThe pandemic spread of COVID-19 has caused worldwide implications on societies and economies. Chest computed tomography (CT) has been found to support both, current diagnostic and disease monitoring. A joint approach to collect, analyze and share clinical and imaging information about COVID-19 in the highest quality possible is urgently needed. MethodsAn evidence-based reporting template was developed for assessing COVID-19 pneumonia using an FDA-approved medical software. The annotation of qualitative and quantitative findings including radiomics features is performed directly on primary imaging data. For data collection, secondary information from the patient history and clinical data such as symptoms and comorbidities are queried. ResultsLicense-royalty free, cloud-based web platform and on-premise deployments are offered. Hospitals can upload, assess, report and if pseudonymized share their COVID-19 cases. The aggregation of radiomics in correlation with rt-PCR, patient history, clinical and radiological findings, systematically documented in a single database, will lead to optimized diagnosis, risk stratification and response evaluation. A customizable analytics dashboard allows the explorative real-time data analysis of imaging features and clinical information. ConclusionsThe COVID-19-Template is based on a systematic, computer-assisted and context-guided approach to collect, analyze and share data. Epidemiological and clinical studies for therapies and vaccine candidates can be implemented in compliance with high data quality, integrity and traceability. An additional explanation video of the COVID-19-Template video is provided via:http://cloud1.mint-medical.de/downloads/player/index.html?v=Covid19StandardizedAssessmentWeb HighlightsO_LIDynamic evidence-based electronic case report form (eCRF) for COVID-19 including documentation of primary imaging data, secondary clinical data and patient history including radiomics features C_LIO_LIComputer-assisted, context-guided reporting approach based on FDA approved medical product software package available free of charge C_LIO_LIData quality, traceability, integrity in open-access web platform C_LIO_LICustomizable analytics dashboard for explorative real-time data analysis of imaging features and clinical information C_LIO_LIHuman and machine-readable data export for clinical trials C_LI

20: COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks
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Posted 24 May 2020

COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks
2,341 downloads medRxiv radiology and imaging

Antonios Makris, Ioannis Kontopoulos, Konstantinos Tserpes

The COVID-19 pandemic in 2020 has highlighted the need to pull all available resources towards the mitigation of the devastating effects of such "Black Swan" events. Towards that end, we investigated the option to employ technology in order to assist the diagnosis of patients infected by the virus. As such, several state-of-the-art pre-trained convolutional neural networks were evaluated as of their ability to detect infected patients from chest X-Ray images. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease, common bacterial pneumonia and healthy individuals. To mitigate the small number of samples, we employed transfer learning, which transfers knowledge extracted by pre-trained models to the model to be trained. The experimental results demonstrate that the classification performance can reach an accuracy of 95% for the best two models.

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