Purpose

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.

Condition

Eligibility

Eligible Ages
Over 18 Years
Eligible Genders
All
Accepts Healthy Volunteers
Yes

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

Study Design

Phase
Study Type
Observational
Observational Model
Cohort
Time Perspective
Retrospective

Arm Groups

ArmDescriptionAssigned Intervention
COVID-19 Patients Single posteroanterior (or "front-on") X-rays collected from COVID-19 patients
  • Device: CovX
    Convolutional neural network for classification of COVID-19 from chest X-rays
Non COVID-19 Patients Single posteroanterior (or "front-on") X-rays collected from subsets of non COVID-19 patients
  • Device: CovX
    Convolutional neural network for classification of COVID-19 from chest X-rays

Recruiting Locations

More Details

NCT ID
NCT04358536
Status
Completed
Sponsor
Dascena

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.

Notice

Study information shown on this site is derived from ClinicalTrials.gov (a public registry operated by the National Institutes of Health). The listing of studies provided is not certain to be all studies for which you might be eligible. Furthermore, study eligibility requirements can be difficult to understand and may change over time, so it is wise to speak with your medical care provider and individual research study teams when making decisions related to participation.