scholarly journals Machine Learning-based Prognostic Modeling using Clinical Data and Quantitative Radiomic Features from Chest CT Images in COVID-19 Patients

2021 ◽  
pp. 104304
Author(s):  
Isaac Shiri ◽  
Majid Sorouri ◽  
Parham Geramifar ◽  
Mostafa Nazari ◽  
Mohammad Abdollahi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Ct Images ◽  
Chest Ct ◽  
Prognostic Modeling

scholarly journals Can Clinical Symptoms and Laboratory Results Predict CT Abnormality? Initial Findings Using Novel Machine Learning Techniques in Children With COVID-19 Infections

2021 ◽  
Vol 8 ◽  
Author(s):  
Huijing Ma ◽  
Qinghao Ye ◽  
Weiping Ding ◽  
Yinghui Jiang ◽  
Minhao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Pediatric Patients ◽  
Clinical Symptoms ◽  
Chest Ct ◽  
Rt Pcr ◽  
Health Crisis ◽  
Children's Hospital ◽  
Laboratory Results ◽  
Children’S Hospital

The rapid spread of coronavirus 2019 disease (COVID-19) has manifested a global public health crisis, and chest CT has been proven to be a powerful tool for screening, triage, evaluation and prognosis in COVID-19 patients. However, CT is not only costly but also associated with an increased incidence of cancer, in particular for children. This study will question whether clinical symptoms and laboratory results can predict the CT outcomes for the pediatric patients with positive RT-PCR testing results in order to determine the necessity of CT for such a vulnerable group. Clinical data were collected from 244 consecutive pediatric patients (16 years of age and under) treated at Wuhan Children's Hospital with positive RT-PCR testing, and the chest CT were performed within 3 days of clinical data collection, from January 21 to March 8, 2020. This study was approved by the local ethics committee of Wuhan Children's Hospital. Advanced decision tree based machine learning models were developed for the prediction of CT outcomes. Results have shown that age, lymphocyte, neutrophils, ferritin and C-reactive protein are the most related clinical indicators for predicting CT outcomes for pediatric patients with positive RT-PCR testing. Our decision support system has managed to achieve an AUC of 0.84 with 0.82 accuracy and 0.84 sensitivity for predicting CT outcomes. Our model can effectively predict CT outcomes, and our findings have indicated that the use of CT should be reconsidered for pediatric patients, as it may not be indispensable.

Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events

2020 ◽  
Vol 9 (2) ◽  
pp. 231-237
Author(s):  
F. A. Sharifullin ◽  
D. D. Dolotova ◽  
T. G. Barmina ◽  
S. S. Petrikov ◽  
L. S. Kokov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ct Angiography ◽  
Clinical Data ◽  
Cerebrovascular Disorders ◽  
Length Of Hospital Stay ◽  
Ct Images ◽  
Lesion Volume ◽  
Cerebrovascular Events ◽  
Stroke Type ◽  
Areas Of Interest

Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the «gold standard» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.

scholarly journals Development and Evaluation of an Early Detection of COVID-19 Pneumonia Using CT Images

2021 ◽  
Author(s):  
D. Eeswar Samhithan ◽  
C. Abarna ◽  
S. Anupama ◽  
S. Devi
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
Time Delay ◽  
Early Detection ◽  
Diagnostic Method ◽  
Virus Disease ◽  
Ct Images ◽  
Chest Ct ◽  
High Rate ◽  
The World

In December 2019, an epidemic of a novel corona virus disease was indentified in Wuhan, China, which quickly spread across the world as a pandemic. This terrible virus had caused respiratory failure and alveolar damage in all, resulting in complete death. The use of a reverse transcription polymerase laboratory test to predict this virus results in a high rate of false positives and a significant time delay. As a result, chest CT images have become a valuable diagnostic method for the Covid-19. In this study, we propose using machine learning to detect and identify the corona, resulting in a timely and accurate report.

scholarly journals Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

2020 ◽  
Author(s):  
Chenglong Liu ◽  
Xiaoyang Wang ◽  
Chenbin Liu ◽  
Qingfeng Sun ◽  
Wenxian Peng
Keyword(s):  
Machine Learning ◽  
Characteristic Curve ◽  
Medical Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Support Vector ◽  
Equal Weight ◽  
Gray Level ◽  
Novel Coronavirus ◽  
Occurrence Matrix

Abstract Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

scholarly journals Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

2020 ◽  
Author(s):  
Chenglong Liu ◽  
Xiaoyang Wang ◽  
Chenbin Liu ◽  
Qingfeng Sun ◽  
Wenxian Peng
Keyword(s):  
Machine Learning ◽  
Characteristic Curve ◽  
Medical Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Support Vector ◽  
Equal Weight ◽  
Gray Level ◽  
Novel Coronavirus ◽  
Occurrence Matrix

Abstract Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented base on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

scholarly journals Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

2020 ◽  
Author(s):  
Chenglong Liu ◽  
Xiaoyang Wang ◽  
Chenbin Liu ◽  
Qingfeng Sun ◽  
Wenxian Peng
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
High Sensitivity ◽  
Ct Images ◽  
Chest Ct ◽  
Learning Framework ◽  
Novel Coronavirus ◽  
Sensitivity Specificity ◽  
Level Performance ◽  
Fold Cross Validation

Abstract Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods To develop and validate an integrated machine learning framework on chest CT images for differentiating COVID-19 from common pneumonia (CP). Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed common pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. Statistical textual features of COVID-19 and CP images were extracted. After feature selection, the reserved features were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers with 10-fold cross-validation. Results The classification accuracy, precision, sensitivity, specificity and F1 score of our proposed method are 91.66%, 97.91%, 85.26%, 98.15% and 91.15% respectively. The AUC of its receiver operating characteristic is 0.98. Conclusions The experimental results indicate that the EBT algorithm with statistical textural features on chest CT for differentiating COVID-19 from common pneumonia achieved high transferability, efficiency, specificity, and impressive accuracy.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy 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. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named 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 the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia 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 and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The 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 dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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