scholarly journals CT-Based Quantitative Assessment of Coronavirus Disease 2019 Using a Deep Learning-Based Segmentation System: A Longitudinal Study

2021 ◽  
Author(s):  
Jianneng Li ◽  
Wei Zuo ◽  
Qiang Lei ◽  
Guihua Jiang ◽  
Jianhao yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Quantitative Analysis ◽  
Automatic Segmentation ◽  
Left Lung ◽  
High Accuracy ◽  
Chest Ct ◽  
The Right ◽  
Deep Learning Model

Abstract BackgroundCoronavirus disease 2019 (COVID-19) is a global catastrophic disease that has severely affected more than 185 countries. The key steps in fighting against COVID-19 involve early detection and tracking of the treatment effects. A large number of studies highlighted computed tomography (CT) as a reliable method for early diagnosis and follow-up monitoring of the disease. However, there are limited data on quantitative analysis of the follow-up images. In this study, we used a deep learning model using a neural network with high accuracy in automatic segmentation and quantification to analyze the infected lesions on chest CT images.MethodsWe used a deep learning model using a neural network with high accuracy in automatic segmentation and quantification to analyze the infected lesions on chest CT images. A total of 14 patients (mean age, 53±14 years; age range, 23–74 years; 42.9% men and 57.1% women) with confirmed mild-type COVID-19 from January 1 to May 7, 2020, were retrospectively reviewed. Initial and follow-up original CT images were collected, and CT quantitative parameters, including percentage of infection (POI) and density variation of pneumonia, were determined.ResultsThe median initial POI was 3.4% (interquartile range, IQR 0.5%–8.4%) for the whole lung, 0.8% (IQR 0.2%–6.7%) for the left lung, and 5.8% (IQR 0.5%–9.7%) for the right lung. The infection was more serious in the right than in the left lung. The infected region mainly involved bilateral lower lobes, more pronounced on the right side. Quantitative CT showed that POI significantly decreased throughout the follow-up period in all 14 patients (p < 0.001). Among them, 50% of the patients had a more significant decrease in POI (51.3%) after a negative nucleic acid test. Moreover, there was a significant decrease in the CT number range of ground-glass opacities (GGO) and consolidation (p < 0.001).ConclusionsThis study demonstrated the quantitative analysis of follow-up CT scans plays an important role in the monitoring of COVID-19 treatment, which could help in treatment planning and standardizing the assessment for discharge.

scholarly journals SUN-932 Successful Response of Temozolomide (TMZ) in two Patients with Metastatic Pheochromocytoma/Paragangliomas (PPGLs)

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Tobias Skrebsky de Almeida ◽  
Roberta P Borges ◽  
Janeczko Laís ◽  
Giovana Caroline Marx Becker ◽  
Ticiana Costa Rodrigues ◽  
...  
Keyword(s):  
Lung Metastases ◽  
Left Lung ◽  
Vena Cava ◽  
Lung Lesion ◽  
Chest Ct ◽  
Complete Regression ◽  
Lung Lesions ◽  
The Right ◽  
Urinary Metanephrines

Abstract Introduction: PPGLs are rare neuroendocrine tumors that arise from chromaffin cells of the adrenal medulla or their neural crest progenitors, being able to secrete catecholamines. Its treatment is primarily surgical; however, for metastatic/inoperable tumors, effective treatments are lacking. The use of TMZ, an oral alkylating agent, has been scarcely reported with variable response rates. We report 2 patients with reasonable clinical, biochemical and structural responses. Case Reports: Case 1) A 14-year old girl presented with neck pain, sweating, hypertension and tachycardia. Urinary hormonal profile revealed metanephrines 80 (up to 320 ug/24h) and normetanephrines 2983 (up to 390 ug/24h). Abdominal MRI showed a 10x6x5 cm retroperitoneal lesion in close contact with celiac trunk, superior mesenteric artery, renal arteries, aorta, left renal vein and vertebral bodies of T10, T11 and T12. A chest CT revealed multiple lung metastases. After 11 months, both the primary abdominal lesion and lung metastases increased in size.. Due to disease severity, after excluding surgical possibilities and confirming diagnosis by lesion biopsy, rescue treatment with TMZ was started for 5 days on a 28-day cycle. After 11 cycles, lung and abdominal lesions decreased more than 30% in size, and urinary metanephrines decreased 53.4%. After 21 cycles, there is no evidence of disease progression. Case 2) A 44-year old female was first diagnosed at the age of 31 with a right adrenal mass invading the kidney and the inferior vena cava associated with hypertension, sweating, headaches and palpitations. She underwent right adrenalectomy and nephrectomy. Immunohistochemistry confirmed the diagnosis of pheochromocytoma. Seven years later, follow-up CT`s showed a 3 x 2 cm liver metastasis, which was resected, and two lung lesions, one located at the right inferior lobe (1.6 cm) and the other at the left superior lobe (0.9 cm), which initially were just followed-up. At this time, a 7-month sorafenib trial was performed but the drug was stopped due to intolerable side effects. After 3 years of follow-up, the lung lesions increased in size and the right lesion was resected, but the patient refused surgery for the remainder left lung lesion. After 1 year, left lung lesion increased to 2.4 cm and mediastinal and paratracheal lymphadenomegaly developed. TMZ in the same aforementioned schedule was prescribed and after 7 cycles a new chest CT revealed complete regression of the lung and lymph node metastases.. Urinary metanephrines were 2.1 times the upper limit of normal before TMZ and decreased to normal range. Conclusion: These cases highlight the promising role of a well-tolerated single drug chemotherapy regimen in severe cases of metastatic and inoperable PPGLs. TMZ could be considered an alternative strategy for the treatment of these cases and, if possible, should be tested in adequate clinical trials.

Abstract WP269: Automatic Segmentation of Cerebral Arteries in MRA TOF Using Deep Learning

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Leonard L Yeo ◽  
Melih Engin ◽  
Robin Lange ◽  
Sethu R Boopathy ◽  
Yang Cunli ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Loss Function ◽  
Automatic Segmentation ◽  
Cerebral Arteries ◽  
Ground Truth ◽  
Learning Model ◽  
Cerebrovascular Diseases ◽  
Intracranial Arteries ◽  
Deep Learning Model

Purpose: Time-of-Flight (TOF) MRA is commonly used for grading cerebrovascular diseases. Analysis of cerebral arteries in MRA TOF is a challenging and time consuming task that would benefit from automation. Established image processing methods for automatic segmentation of cerebral arteries suffer from common artefacts such as kissing vessels (when two nearby vessels touch) and signal intensity drop (especially in the presence of pathology). Artificial intelligence models are promising candidates for resolving such artefacts. Here, we propose and assess the performance of a deep learning model for automatic segmentation of cerebral arteries in MRA TOF which is robust to common MRI artefacts. Methods: A 3D convolutional neural network (CNN) is proposed for automatic segmentation of intracranial arteries in MRA TOF. The neural network is trained with a custom loss function and residual blocks to penalize the occurrence of common artefacts such as kissing vessels. The model is trained and tested on a dataset consisting of 82 subjects (50 healthy volunteers and 32 patients with intracranial stenosis) following a 3-fold cross-validation method, i.e. 3 models are trained where each model is blind to one-third of the data in the training process to avoid bias. Manual segmentation of the arteries done by an expert reader are used as ground-truth for training and testing the model. Results: The proposed deep learning model achieved excellent accuracy compared against the ground truth (Dice score 0.89). Our proposed deep learning model outperformed a state-of-the-art neural network for image segmentation (3DU-Net, Dice score 0.85) and resulted in considerably less occurences of artefacts such as kissing vessels (9% of cases had segmentation artefacts for our model vs 16% for 3D U-Net). The proposed deep learning model was fast, taking only 2 seconds to produce a 3D model of the arteries on a laptop with a dedicated GPU. Conclusion: The proposed deep learning model accurately segments intracranial arteries in MRA TOF and is robust to common artefacts of MR imaging thanks to implementation of a custom loss function. The model can potentially increase the accuracy and speed of grading cerebrovascular diseases.

scholarly journals Coronavirus 2019 (COVID-19) Detection Based on Deep Learning

2020 ◽  
Vol 23 (4) ◽  
pp. 408-415
Author(s):  
Toqa Abd Ul-Mohsen Sadoon ◽  
Mohammed Hussein Ali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Imaging Techniques ◽  
Treatment Decision ◽  
High Accuracy ◽  
The Other ◽  
Test Accuracy ◽  
Proposed Model ◽  
Deep Learning Model

Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.

CASE REPORT: SURGICAL MANAGEMENT OF LUMBAR COMPRESSION FRACTURE

2019 ◽  
Vol 1 (4) ◽  
Author(s):  
Yustinus Robby Budiman Gondowardojo ◽  
Tjokorda Gde Bagus Mahadewa
Keyword(s):  
Spinal Cord ◽  
Left Lung ◽  
Lumbar Vertebrae ◽  
Compression Fracture ◽  
High Mobility ◽  
Early Mobilization ◽  
Cord Injury ◽  
Cardiothoracic Surgeon ◽  
The Right

The lumbar vertebrae are the most common site for fracture incident because of its high mobility. The spinal cord injury usually happened as a result of a direct traumatic blow to the spine causing fractured and compressed spinal cord. A 38-year-old man presented with lumbar spine’s compression fracture at L2 level. In this patient, decompression laminectomy, stabilization, and fusion were done by posterior approach. The operation was successful, according to the X-Ray and patient’s early mobilization. Pneumothorax of the right lung and pleural effusion of the left lung occurred in this patient, so consultation was made to a cardiothoracic surgeon. Chest tube and WSD insertion were performed to treat the comorbidities. Although the patient had multiple trauma that threat a patient’s life, the management was done quickly, so the problems could be solved thus saving the patient’s life. After two months follow up, the patient could already walk and do daily activities independently.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

scholarly journals Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1002
Author(s):  
Mohammad Khishe ◽  
Fabio Caraffini ◽  
Stefan Kuhn
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Iterative Process ◽  
High Accuracy ◽  
X Ray ◽  
Starting Point ◽  
Reliable Model ◽  
Minimum Number ◽  
Chest X Ray ◽  
Deep Learning Model

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

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