scholarly journals Sensitivity and specificity of artificial intelligence with Microsoft Azure in detecting pneumothorax in emergency department: A pilot study

2020 ◽  
pp. 102490792094899
Author(s):  
Kwok Hung Alastair Lai ◽  
Shu Kai Ma
Keyword(s):  
Artificial Intelligence ◽  
Emergency Department ◽  
Sensitivity And Specificity ◽  
National Institutes Of Health ◽  
Dramatic Improvement ◽  
X Ray ◽  
Training Images ◽  
Normal Chest ◽  
Chest X Ray ◽  
Microsoft Azure

Background: Artificial intelligence is becoming an increasingly important tool in different medical fields. This article aims to evaluate the sensitivity and specificity of artificial intelligence trained with Microsoft Azure in detecting pneumothorax. Methods: A supervised learning artificial intelligence is trained with a collection of X-ray images of pneumothorax from National Institutes of Health chest X-ray dataset online. A subset of the image dataset focused on pneumothorax is used in training. Two artificial intelligence programs are trained with different numbers of training images. After the training, a collection of pneumothorax X-ray images from patient attending emergency department is retrieved through the Clinical Data Analysis & Reporting System. In total, 115 pneumothorax patients and 60 normal inpatients are recruited. The pneumothorax chest X-ray and the resolution chest X-ray of the above patient group and a collection of normal chest X-ray from inpatients without pneumothorax will be retrieved, and these three sets of images will then undergo testing by artificial intelligence programs to give a probability of being a pneumothorax X-ray. Results: The sensitivity of artificial intelligence-one is 33.04%, and the specificity is at least 61.74%. The sensitivity of artificial intelligence-two is 46.09%, and the specificity is at least 71.30%. The dramatic improvement of 46.09% in sensitivity and improvement of 15.48% in specificity by addition of around 1000 X-ray images is encouraging. The mean improvement of AI-two over AI-one is 19.7% increase in probability difference. Conclusions: We should not rely on artificial intelligence in diagnosing pneumothorax X-ray solely by our models and more training should be expected to explore its full function.

scholarly journals A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

2020 ◽  
Vol 8 (2) ◽  
pp. 120-127
Author(s):  
Mohammad Hosein Sadeghi ◽  
Hamid Omidi ◽  
Sedigheh Sina
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Model Performance ◽  
Area Under The Curve ◽  
Google Scholar ◽  
X Ray ◽  
Source Codes ◽  
Specificity And Sensitivity ◽  
Chest X Ray

Background: In this study, the artificial intelligence (AI) techniques used for the detection of coronavirus disease 2019 (COVID-19) from the chest x-ray were reviewed. Methods: PubMed, arXiv, and Google Scholar were used to search for AI studies. Results: A total of 20 papers were extracted from Google Scholar, 14 from arXiv, and 5 from PubMed. In 17 papers, publicly available datasets and in 3 papers, independent datasets were used. 10 papers disclosed source codes. Nine papers were about creating a novel AI software, 8 papers reported the modification of the existing AI models, and 3 compared the performance of the existing AI software programs. All papers have used deep learning as AI technique. Most papers reported accuracy, specificity, and sensitivity of the models, and also the area under the curve (AUC) for investigation of the model performance for the prediction of COVID-19. Nine papers reported accuracy, sensitivity, and specificity. The number of datasets used in the studies ranged from 50 to 94323. The accuracy, sensitivity, and specificity of the models ranged from 0.88 to 0.98, 0.80 to 1.00, and 0.70 to 1.00, respectively. Conclusion: The studies revealed that AI can help human in fighting the new Coronavirus.

scholarly journals Training Artificial Intelligence Neural Networks to Detect Devices on a Chest X-ray, a Pathway to COVID-19 Testing

2020 ◽  
Vol 112 (5) ◽  
pp. S50
Author(s):  
Zachary Eller ◽  
Michelle Chen ◽  
Jermaine Heath ◽  
Uzma Hussain ◽  
Thomas Obisean ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
X Ray ◽  
Chest X Ray

AB0796 THE EVALUATION OF LUNG DIAGNOSTIC PROCEDURE AT THE ONSET OF INFLAMMATORY RHEUMATIC DISEASES WITH INTERSTITIAL LUNG DISEASE

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1422.3-1423
Author(s):  
T. Hoffmann ◽  
P. Oelzner ◽  
F. Marcus ◽  
M. Förster ◽  
J. Böttcher ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease ◽  
Sensitivity And Specificity ◽  
Lung Function Test ◽  
Inflammatory Rheumatic Diseases ◽  
X Ray ◽  
Function Test ◽  
Chest X Ray

Background:Interstitial lung disease (ILD) in inflammatory rheumatic diseases (IRD) is associated with increased mortality. Moreover, the lung is one of the most effected organs on IRD. Consequently, screening methods were required to the detect ILD in IRD.Objectives:The objective of the following study is to evaluate the diagnostic value of lung function test, chest x-ray and HR-CT of the lung in the detection of ILD at the onset of IRD.Methods:The study is designed as a case-control study and includes 126 patients with a newly diagnosed IRD. It was matched by gender, age and the performance of lung function test and chest x-ray. The sensitivity and specificity were verified by crosstabs and receiver operating characteristic (ROC) curve analysis. The study cohort was divided in two groups (ILD group: n = 63 and control group: n = 63). If possible, all patients received a lung function test and optional a chest x-ray. Patients with pathological findings in the screening tests (chest x-ray or reduced diffusing capacity for carbon monoxide (DLCO) < 80 %) maintained a high-resolution computer tomography (HR-CT) of the lung. Additionally, an immunological bronchioalveolar lavage was performed in the ILD group as gold standard for the detection of ILD.Results:The DLCO (< 80 %) revealed a sensitivity of 83.6 % and specificity of 45.8 % for the detection of ILD. Other examined parameter of lung function test showed no sufficient sensitivity as screening test (FVC = Forced Vital Capacity, FEV1 = Forced Expiratory Volume in 1 second, TLC = Total Lung Capacity, TLCO = Transfer factor of the Lung for carbon monoxide). Also, a combination of different parameter did not increase the sensitivity. The sensitivity and specificity of chest x-ray for the verification of ILD was 64.2 % versus 73.6 %. The combination of DLCO (< 80 %) and chest x-ray showed a sensitivity with 95.2 % and specificity with 38.7 %. The highest sensitivity (95.2 %) and specificity (77.4 %) was observed for the combination of DLCO (< 80 %) and HR-CT of the lung.Conclusion:The study highlighted that a reduced DLCO in lung function test is associated with a lung involvement in IRD. DLCO represented a potential screening parameter for lung manifestation in IRD. Especially patients with suspected vasculitis should receive an additional chest x-ray. Based on the high sensitivity of DLCO in combination with chest x-ray or HR-CT for the detection of ILD in IRD, all patients with a reduced DLCO (< 80%) should obtained an imaging of the lung.Disclosure of Interests:None declared

A normal chest X-ray does not rule out the patient’s version

2021 ◽  
Author(s):  
Elena Forcén ◽  
María José Bernabé ◽  
Roberto Larrosa-Barrero
Keyword(s):  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Rule Out

scholarly journals Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 434
Author(s):  
Anca Nicoleta Marginean ◽  
Delia Doris Muntean ◽  
George Adrian Muntean ◽  
Adelina Priscu ◽  
Adrian Groza ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Negative Impact ◽  
X Ray ◽  
Domain Specific ◽  
Direct Training ◽  
Public Data ◽  
Normal Chest ◽  
Chest X Ray ◽  
The Stability ◽  
Definition Of

It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset.

scholarly journals Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

2021 ◽  
Vol 11 (2) ◽  
pp. 411-424 ◽  
Author(s):  
José Daniel López-Cabrera ◽  
Rubén Orozco-Morales ◽  
Jorge Armando Portal-Diaz ◽  
Orlando Lovelle-Enríquez ◽  
Marlén Pérez-Díaz
Keyword(s):  
Artificial Intelligence ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray

scholarly journals Correction to: Chest x-ray severity score in COVID-19 patients on emergency department admission: a two-centre study

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Cristian Giuseppe Monaco ◽  
Federico Zaottini ◽  
Simone Schiaffino ◽  
Alessandro Villa ◽  
Gianmarco Della Pepa ◽  
...  
Keyword(s):  
Emergency Department ◽  
Severity Score ◽  
X Ray ◽  
Emergency Department Admission ◽  
Centre Study ◽  
Chest X Ray

An amendment to this paper has been published and can be accessed via the original article.

The normal chest X-ray

2017 ◽  
pp. 15-34
Author(s):  
Thomas Kurka
Keyword(s):  
X Ray ◽  
Normal Chest ◽  
Chest X Ray

scholarly journals Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Ihssan S. Masad
Keyword(s):  
Artificial Intelligence ◽  
Input Image ◽  
Image Size ◽  
X Ray ◽  
Softmax Classifier ◽  
Non Invasive ◽  
Efficient Detection ◽  
X Ray Imaging ◽  
Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

Sign in / Sign up

Export Citation Format

Share Document