scholarly journals Clinical and Deep-Learning Based Quantitative Serial Chest CT Features of The COVID-19 Disease: Association with Clinical Subtypes and A Follow-Up Study

2020 ◽  
Author(s):  
Xiaoyun Su ◽  
Yuxi Jia ◽  
Qing Zhu ◽  
Bo Liu ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Chest Ct ◽  
Image Process ◽  
Quantitative Ct ◽  
Abnormal Rate ◽  
D Dimer ◽  
Traditional Image ◽  
Median Interval

Abstract Background: To explore the clinical features and deep-learning (DL) based quantitative CT finding’s applications and evolution as well as the correlations in COVID-19.Methods: 273 chest CT scans (median interval, 6 days) from 75 COVID-19 RT-PCT positive patients (53 moderate and 22 severe) were included. Quantification parameters, such as CT value distribution, lesion (abnormal), GGO, consolidation rates, Hellinger distance and IOU, were automatically extracted from CT images by a combination of traditional image process algorithm and DL network. Clinical characteristics were also collected and analysed.Results: The hypertension and diabetes were more common in severity. The CRP, ESP, LDH and D-dimer were higher while LYM and LYM% lower in severity (P < 0.05). The DL network was detected the lesions to obtain quantitively CT indicators, with fast to process a chest CT images (average time, 2.2s) and high overlap with radiologist. The hellinger, abnormal, GGO, consolidation rates and HU values were higher and the IOU lower in severity than moderate patients (P < 0.05). The largest AUC was 0.943, using the cutoff value of 10.5% for abnormal rate. The CT score have postive correlations with CRP, D-dimer and ESR (P < 0.05). The increased levels of ESR and D-dimer were positively correlated with abnormal, consolidation and GGO rates (P < 0.05). Investigation for quantitative CT changes were performed in three periods: 1) 1-2 weeks, CT score and abnormal rate were increased. The GGO converted to consolidation in severity; 2) 2-5 weeks, CT scores stable trend, while abnormal and GGO rates had upward trend in severity; 3) > 5weeks, CT score and abnormal rate have decreased.Conclusions: There were three phases of two patterns’ evolutionary trends in quantitative CT findings with differences in two groups, and have correlations with laboratory markers, which helpful for evaluating severity and prognosis in COVID-19 patients.

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.

scholarly journals Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
He Sui ◽  
Ruhang Ma ◽  
Lin Liu ◽  
Yaozong Gao ◽  
Wenhai Zhang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Ct Images ◽  
Chest Ct ◽  
Validation Dataset ◽  
Esophageal Wall ◽  
Detection Model ◽  
Model Training ◽  
Sensitivity Specificity

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

scholarly journals Deep Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

2021 ◽  
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat
Keyword(s):  
Deep Learning ◽  
Diagnostic Tests ◽  
Ct Images ◽  
Rapid Diagnosis ◽  
Chest Ct ◽  
Visual Features ◽  
Learning Module ◽  
Volumetric Ct ◽  
The Cost

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

2021 ◽  
pp. 981-990
Author(s):  
Mostafa El Habib Daho ◽  
Amin Khouani ◽  
Mohammed El Amine Lazouni ◽  
Sidi Ahmed Mahmoudi
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Learning Model

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

2020 ◽  
Vol 30 (12) ◽  
pp. 6517-6527 ◽  
Author(s):  
Qianqian Ni ◽  
Zhi Yuan Sun ◽  
Li Qi ◽  
Wen Chen ◽  
Yi Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Chest Ct ◽  
Learning Approach

scholarly journals Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

2020 ◽  
Author(s):  
A. Amyar ◽  
R. Modzelewski ◽  
S. Ruan
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
The Novel ◽  
Imaging Analysis ◽  
Proposed Model ◽  
Fast Spreading ◽  
Novel Coronavirus ◽  
Deep Learning Model

ABSTRACTThe fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

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