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

NCT ID: NCT04358536

Last Updated: 2020-04-24

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 230 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-04-01
• Study Completion Date: 2020-04-17

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.

Conditions

COVID-19

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

COVID-19 Patients

Single posteroanterior (or "front-on") X-rays collected from COVID-19 patients

CovX

Intervention Type: DEVICE

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

CovX

Intervention Type: DEVICE

Convolutional neural network for classification of COVID-19 from chest X-rays

Interventions

CovX

Convolutional neural network for classification of COVID-19 from chest X-rays

Intervention Type: DEVICE

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Dascena

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Dascena

Oakland, California, United States

Countries

United States

Other Identifiers

04202002

Identifier Type: -

Identifier Source: org_study_id