Classification of COVID-19 Infection in Posteroanterior Chest X-rays

Study Overview
=================
Brief Summary
-----------------
The objective of this study is to assess three configurations of two convolutional deep neural network architectures for the classification of COVID-19 PCX images.

Detailed Description
-----------------
The December 2019 outbreak of COVID-19 has now evolved into a public health emergency of global concern. Given the rapid spread of infection, the rapid depletion of hospital resources due to high influxes of patients, and the current absence of specific therapeutic drugs and vaccines for treatment of COVID-19 infection, it is essential to detect onset of the disease at its early stages. Radiological examinations, the most common of which are posteroanterior chest X-ray (PCX) images, play an important role in the diagnosis of COVID-19. The objective of this study is to assess three configurations of two convolutional deep neural network architectures for the classification of COVID-19 PCX images. The primary experimental dataset consisted of 115 COVID-19 positive and 115 COVID-19 negative PCX images, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images (230 total images). Two common convolutional neural network architectures were used, VGG16 and DenseNet121, the former initially configured with off-the-shelf (OTS) parameters and the latter with either OTS or exclusively X-ray trained (XRT) parameters. The OTS parameters were derived from training on the ImageNet dataset, while the XRT parameters were obtained from training on the NIH chest X-ray dataset, ChestX-ray14. A final, densely connected layer was added to each model, the parameters of which were trained and validated on 87% of images from the experimental dataset, for the task of binary classification of images as COVID-19 positive or COVID-19 negative. Each model was tested on a hold-out set consisting of the other 13% of images. Performance metrics were calculated as the average over five random 80%-20% splits of the images into training and validation sets, respectively.

Official Title
-----------------
Classification of COVID-19 Infection in Posteroanterior Chest X-rays With Common Deep Learning Architectures

Conditions
-----------------
COVID-19

Intervention / Treatment
-----------------
* Device: CovX


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Single PCX images collected from patients over 18 years of age Exclusion Criteria: CT scans composed of multiple concerted X-rays Single PCX images collected from patients under 18 years of age

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| COVID-19 Patients<br>Single posteroanterior (or front-on) X-rays collected from COVID-19 patients | Device: CovX<br>* Convolutional neural network for classification of COVID-19 from chest X-rays<br>|
| Non COVID-19 Patients<br>Single posteroanterior (or front-on) X-rays collected from subsets of non COVID-19 patients | Device: CovX<br>* Convolutional neural network for classification of COVID-19 from chest X-rays<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification of COVID-19 | Identification of COVID-19 infection from chest X-ray analysis | Through study completion, an average of 2 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
machine learning, X-ray, classification, COVID-19, posteroanterior, convolutional neural network architectures