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

COVID-19 Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04358536
• Other study ID #: 04202002
• Status: Completed
• Start date: April 1, 2020
• Est. completion date: April 17, 2020

Study information

• Verified date: April 2020
• Source: Dascena
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 230
• Est. completion date: April 17, 2020
• Est. primary completion date: April 17, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• COVID-19
• Infection

Intervention

Device:
• CovX
Convolutional neural network for classification of COVID-19 from chest X-rays

Locations

Country	Name	City	State
United States	Dascena	Oakland	California

Sponsors (1)

Lead Sponsor	Collaborator
Dascena

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Identification of COVID-19	Identification of COVID-19 infection from chest X-ray analysis	Through study completion, an average of 2 months

External Links

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