scholarly journals CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tahereh Javaheri ◽  
Morteza Homayounfar ◽  
Zohreh Amoozgar ◽  
Reza Reiazi ◽  
Fatemeh Homayounieh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Treatment Options ◽  
Community Acquired Pneumonia ◽  
Ct Imaging ◽  
Small Sample ◽  
Rt Pcr ◽  
User Privacy ◽  
Screening Process ◽  
Imaging Detection

AbstractCoronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

scholarly journals Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging

2021 ◽  
pp. 1-1
Author(s):  
Rajesh Kumar ◽  
Abdullah Aman Khan ◽  
Jay Kumar ◽  
A. Zakria ◽  
Noorbakhsh Amiri Golilarz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Imaging ◽  
Learning Models

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoguo Zhang ◽  
Dawei Wang ◽  
Jiang Shao ◽  
Song Tian ◽  
Weixiong Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Machine Learning Algorithms ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Medical Centers ◽  
Global Pandemic ◽  
Diagnostic Time ◽  
Logistic Regression Classifier

AbstractSince its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

scholarly journals 549. Clinical Characteristics and Outcomes of Patients with COVID-19 treated with Convalescent Plasma in Miami, Florida

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S340-S341
Author(s):  
Shweta Anjan ◽  
Dimitra Skiada ◽  
Miriam Andrea Duque Cuartas ◽  
Douglas Salguero ◽  
David P Serota ◽  
...  
Keyword(s):  
Treatment Options ◽  
Disease Outbreaks ◽  
Nasopharyngeal Swab ◽  
Rt Pcr ◽  
Health Crisis ◽  
Infectious Disease Outbreaks ◽  
Reactive Protein ◽  
High Acuity ◽  
Us Fda ◽  
Oxygen Requirements

Abstract Background The Coronavirus disease of 2019 (COVID-19) global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in unprecedented mortality, impacted society, and strained healthcare systems, yet sufficient data regarding treatment options are lacking. Convalescent plasma, used since 1895 for infectious disease outbreaks, offers promise as a treatment option for COVID-19. Methods This is a retrospective study of patients diagnosed by a nasopharyngeal swab SARS-CoV-2 reverse transcriptase–polymerase chain reaction (RT-PCR), who received convalescent plasma between April to June 2020 at two large hospitals in Miami, Florida, as part of the US FDA Expanded Access Program for COVID-19 convalescent plasma (CCP). Results A total of 23 patients received CCP, 13 (57%) had severe COVID-19 disease, while 8 (35%) had critical or critical with multiorgan dysfunction. Median time of follow up was 26 (range, 7–79) days. Overall, 11 (48%) survived to discharge, 6 (26%) died, while 6 (26%) are currently hospitalized. All deaths reported were due to septic shock from secondary infections. 15 (65%) showed improvement in oxygen requirements 7 days post CCP transfusion. Measured inflammatory markers, c-reactive protein, lactate dehydrogenase, ferritin and d-dimer improved 7 days post transfusion in 13 (57%) patients. No adverse events due to the transfusion were reported. 10 (43.4%) patients had a negative SARS-CoV-2 RT-PCR at a median of 14.5 (range, 4–31) days after receiving convalescent plasma. Conclusion Administration of convalescent plasma was found to be safe, with favorable outcomes in this small cohort of relatively high acuity patients. Larger studies including control arms are needed to establish the efficacy of convalescent plasma on clinical and virologic outcomes for patients with COVID-19. Table Disclosures All Authors: No reported disclosures

scholarly journals The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vikram rao Bollineni ◽  
Koenraad Hans Nieboer ◽  
Seema Döring ◽  
Nico Buls ◽  
Johan de Mey
Keyword(s):  
Ct Scan ◽  
Predictive Value ◽  
Ct Imaging ◽  
Chest Ct ◽  
University Hospital ◽  
Rt Pcr ◽  
Ct Findings ◽  
Epidemic Area ◽  
Chest Ct Scan ◽  
Pcr Test

Abstract Background To evaluate the clinical value of the chest CT scan compared to the reference standard real-time polymerase chain reaction (RT-PCR) in COVID-19 patients. Methods From March 29th to April 15th of 2020, a total of 240 patients with respiratory distress underwent both a low-dose chest CT scan and RT-PCR tests. The performance of chest CT in diagnosing COVID-19 was assessed with reference to the RT-PCR result. Two board-certified radiologists (mean 24 years of experience chest CT), blinded for the RT-PCR result, reviewed all scans and decided positive or negative chest CT findings by consensus. Results Out of 240 patients, 60% (144/240) had positive RT-PCR results and 89% (213/240) had a positive chest CT scans. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of chest CT in suggesting COVID-19 were 100% (95% CI: 97–100%, 144/240), 28% (95% CI: 19–38%, 27/240), 68% (95% CI: 65–70%) and 100%, respectively. The diagnostic accuracy of the chest CT suggesting COVID-19 was 71% (95% CI: 65–77%). Thirty-three patients with positive chest CT scan and negative RT-PCR test at baseline underwent repeat RT-PCR assay. In this subgroup, 21.2% (7/33) cases became RT-PCR positive. Conclusion Chest CT imaging has high sensitivity and high NPV for diagnosing COVID-19 and can be considered as an alternative primary screening tool for COVID-19 in epidemic areas. In addition, a negative RT-PCR test, but positive CT findings can still be suggestive of COVID-19 infection.

scholarly journals COVID-19 serological testing for Healthcare Workers in Lombardy, Italy

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
G Del Castillo ◽  
A Castrofino ◽  
F Grosso ◽  
A Barone ◽  
L Crottogini ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Geographical Area ◽  
Healthcare Facility ◽  
Nasopharyngeal Swab ◽  
Infection Prevalence ◽  
Serological Testing ◽  
Prevention Policy ◽  
Rt Pcr ◽  
Serological Tests ◽  
Screening Process

Abstract Issue COVID-19 pandemic began in Italy on February 20th, 2020. Since the beginning of the emergency Healthcare Workers' (HCWs) involvement was prominent, mainly due to direct assistance to COVID-19 patients. Therefore, we implemented a prevention policy for HCW screening through serological and RT-PCR testing. Description of the problem HCW screening for SARS-CoV-2 infection is essential for prevention and control of the pandemic. Lombardy's Healthcare authorities settled a screening process for HCWs divided into three steps: 1) body temperature assessment at the beginning and the end of work shift, if fever > 37.5 °C was present the HCW was sent back home and a nasopharyngeal swab was performed; 2) progressive recruitment for serological testing; 3) on those positive to IgG a nasopharyngeal swab was performed and tested for viral RNA by RT-PCR. Results Among 79185 HCW tested, 9589 (12%) were positive on serological IgG testing. Of the 9589 positive a nasopharyngeal swab was performed on 6884. Of these 358 (5%) tested positive and the remaining 6526 (95%) negative to RT-PCR. We calculated a Positive Predictive Value of 5.2%. The rate of positive serological tests for each Healthcare facility varied between 0% and 78%. Five percent of all facilities, belonging to Brescia, Bergamo and Cremona area, reported a positivity rate higher than 40% in HCWs. A second cluster (18% of all facilities), involving the same geographical area, reported a rate between 20% and 40%, whereas the remaining facilities (76%) of the region a rate <20%. Lessons Serological IgG testing can be, if followed by immediate nasopharyngeal swab testing, a valid screening intervention on asymptomatic HCWs especially in a high infection prevalence setting. Key messages Serological IgG testing can be, if followed by immediate nasopharyngeal swab testing, a valid screening intervention on asymptomatic HCWs. Infection prevention in HCW may benefit from a screening campaign especially in high prevalence settings.

Social Distancing Using AI and Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 1816-1822
Author(s):  
Mr. Kiran Mudaraddi
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Open Source ◽  
Suggested Method ◽  
Video Frame ◽  
Social Distancing ◽  
The Social ◽  
The Impact

The paper presents a deep learning-based methodology for detecting social distancing in order to assess the distance between people in order to mitigate the impact of the coronavirus pandemic. The input was a video frame from the camera, and the open-source object detection was pre-trained. The outcome demonstrates that the suggested method is capable of determining the social distancing measures between many participants in a video.

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Image

2021 ◽  
Author(s):  
J.M. Murray ◽  
P. Pfeffer ◽  
R. Seifert ◽  
A. Hermann ◽  
J. Handke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Atherosclerotic Plaque ◽  
State Of The Art ◽  
The Mean ◽  
Time Savings ◽  
Plaque Quantification ◽  
Atherosclerosis Research ◽  
Hematoxylin Eosin ◽  
Microscopy Images

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool. It includes 2 models, trained and tested on 1089 hematoxylin-eosin stained mouse model atherosclerotic brachiocephalic artery sections. The models were compared to 3 human raters. Vesseg can be accessed at https://vesseg .online or downloaded. The models show mean Soerensen-Dice scores of 0.91±0.15 for plaque and 0.97±0.08 for lumen pixels. The mean accuracy is 0.98±0.05. Vesseg is already in active use, generating time savings of >10 minutes per slide. Conclusions: Vesseg brings state-of-the-art deep learning methods to atherosclerosis research, providing drastic time savings, while allowing for continuous improvement of models and the underlying pipeline.

Confidence intervals for a difference between lognormal means in cluster randomization trials

2014 ◽  
Vol 26 (2) ◽  
pp. 598-614 ◽  
Author(s):  
Julia Poirier ◽  
GY Zou ◽  
John Koval
Keyword(s):  
Confidence Intervals ◽  
Community Acquired Pneumonia ◽  
Small Sample ◽  
Cluster Randomization ◽  
Critical Pathway ◽  
Arithmetic Means ◽  
Multiple Parameters ◽  
The Difference ◽  
Using Data ◽  
Small Sample Sizes

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

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