Clinical trial NCT04358536: Classification of COVID-19 Infection in Posteroanterior Chest X-rays
Official title: Classification of COVID-19 Infection in Posteroanterior Chest X-rays With Common Deep Learning Architectures
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID-19
Keywords: machine learning
X-ray
classification
COVID-19
posteroanterior
convolutional neural network architectures
Lead sponsor: Dascena
Sponsor class: INDUSTRY
First posted: 2020-04-24
Enrollment: 230
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 115 COVID-19 single PCX images and 115 non COVID-19 single PCX images (230 images total), collected from 81 unique COVID-19 patients and 91 unique non COVID-19 patients.

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.

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

Primary outcomes:
measure: Identification of COVID-19
timeFrame: Through study completion, an average of 2 months
Secondary outcomes:
Other outcomes:
Locations:
city: Oakland
country: United States
facility: Dascena
state: California
Study officials:
References: