An Artificial Intelligence Based Technique for COVID-19 Diagnosis from 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.

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

10.21203/rs.3.rs-66836/v1 ◽

2020 ◽

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.

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An Explainable Artificial Intelligence based Prospective Framework for COVID-19 Risk Prediction

10.1101/2021.03.02.21252269 ◽

2021 ◽

Author(s):

Vishal Sharma ◽

Piyush ◽

Samarth Chhatwal ◽

Bipin Singh

Keyword(s):

Artificial Intelligence ◽

Risk Prediction ◽

Infection Risk ◽

Alternative Methods ◽

Confirmatory Test ◽

Rt Pcr ◽

Web Based ◽

X Ray ◽

Explainable Artificial Intelligence ◽

Chest X Ray

Given the spread of COVID-19 to vast geographical regions and populations, it is not feasible to undergo or recommend the RT-PCR based tests to all individuals with flu-like symptoms. The reach of RT-PCR based testing is still limited due to the high cost of the test and huge population in few countries. Thus, alternative methods for COVID-19 infection risk prediction can be useful. We built an explainable artificial intelligence (AI) based integrated web-based prospective framework for COVID-19 risk prediction. We employed a two-step procedure for the non-clinical prediction of COVID19 infection risk. In the first step we assess the initial risk of COVID19 infection based on carefully selected parameters associated with COVID-19 positive symptoms from recent research. Generally, X-ray scans are cheaper and easily available in most government and private health centres. Therefore, based on the outcome of the computed initial risk in first step, we further provide an optional prediction using the chest X-ray scans in the second step of our proposed AI based prospective framework. Since there is a bottleneck to undergo an expensive RT-PCR based confirmatory test in economically backward nations, this is a crucial part of our explainable AI based prospective framework. The initial risk assessment outcome is analysed in combination with the advanced deep learning-based analysis of chest X-ray scans to provide an accurate prediction of COVID-19 infection risk. This prospective web-based AI framework can be employed in limited resource settings after clinical validation in future. The cost and time associated with the adoption of this prospective AI based prospective framework will be minimal and hence it will be beneficial to majority of the population living in low-income settings such as small towns and rural areas that have limited access to advanced healthcare facilities.

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Automated chest radiograph diagnosis: A Twofer for Tuberculosis and Covid-19

10.1101/2020.10.13.20178483 ◽

2020 ◽

Author(s):

Mitushi Verma ◽

Deepak Patkar ◽

Madhura Ingalharikar ◽

Amit Kharat ◽

Pranav Ajmera ◽

...

Keyword(s):

Artificial Intelligence ◽

X Ray ◽

Recent Onset ◽

Radiology Reports ◽

Fast Spreading ◽

Digital Chest ◽

Chest X Ray ◽

End To End ◽

The Times

AbstractCoronavirus disease (Covid 19) and Tuberculosis (TB) are two challenges the world is facing. TB is a pandemic which has challenged mankind for ages and Covid 19 is a recent onset fast spreading pandemic. We study these two conditions with focus on Artificial Intelligence (AI) based imaging, the role of digital chest x-ray and utility of end to end platform to improve turnaround times. Using artificial intelligence assisted technology for triage and creation of structured radiology reports using an end to end platform can ensure quick diagnosis. Changing dynamics of TB screening in the times of Covid 19 pandemic have resulted in bottlenecks for TB diagnosis. The paper tries to outline two types of use cases, one is COVID-19 screening in a hospital-based scenario and the other is TB screening project in mobile van setting and discusses the learning of these models which have both used AI for prescreening and generating structured radiology reports.

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Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia

Computational Intelligence and Neuroscience ◽

10.1155/2020/8876798 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Zhenjia Yue ◽

Liangping Ma ◽

Runfeng Zhang

Keyword(s):

Artificial Intelligence ◽

Visual Analysis ◽

Clinical Image ◽

Human Beings ◽

Accuracy Rate ◽

Network Algorithms ◽

X Ray ◽

Image Diagnosis ◽

Chest X Ray ◽

Low Efficiency

As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient’s X-ray chest radiograph by an experienced doctor takes about 5 to 15 minutes. When cases are concentrated, this will undoubtedly put tremendous pressure on the doctor’s clinical diagnosis. Therefore, relying on the naked eye of the imaging doctor has very low efficiency. Hence, the use of artificial intelligence for clinical image diagnosis of pneumonia is a necessary thing. In addition, artificial intelligence recognition is very fast, and the convolutional neural networks (CNNs) have achieved better performance than human beings in terms of image identification. Therefore, we used the dataset which has chest X-ray images for classification made available by Kaggle with a total of 5216 train and 624 test images, with 2 classes as normal and pneumonia. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results, from which we improved MobileNet’s network structure and achieved a higher accuracy rate than other methods. Furthermore, the improved MobileNet’s network could also extend to other areas for application.

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