Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach

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.

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Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem

Health and Technology ◽

10.1007/s12553-021-00609-8 ◽

2021 ◽

Author(s):

José Daniel López-Cabrera ◽

Rubén Orozco-Morales ◽

Jorge Armando Portal-Díaz ◽

Orlando Lovelle-Enríquez ◽

Marlén Pérez-Díaz

Keyword(s):

Artificial Intelligence ◽

Learning Problem ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

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The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Diagnostics ◽

10.3390/diagnostics11122206 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2206

Author(s):

Dana Li ◽

Lea Marie Pehrson ◽

Carsten Ammitzbøl Lauridsen ◽

Lea Tøttrup ◽

Marco Fraccaro ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Assessment Tool ◽

Cochrane Library ◽

Original Research ◽

Quality Assessment Tool ◽

Bias Assessment ◽

X Ray ◽

Chest X Ray ◽

Sensitivity Specificity

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

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DenTcov: Deep Transfer Learning-Based Automatic Detection of Coronavirus Disease (COVID-19) Using Chest X-ray Images

Digital Technologies and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-73882-2_88 ◽

2021 ◽

pp. 967-977

Author(s):

Youssra El Idrissi El-Bouzaidi ◽

Otman Abdoun

Keyword(s):

Transfer Learning ◽

Automatic Detection ◽

X Ray ◽

Chest X Ray

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

Hong Kong Journal of Emergency Medicine ◽

10.1177/1024907920948994 ◽

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.

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