COVID-19NCT04358536
Classification of COVID-19 Infection in Posteroanterior Chest X-rays
An observational study of COVID-19, run by Dascena at 1 site in United States.
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- COVID-19
- What is being tested?
- CovX
- Type of treatment
- Medical device
- How many people
- 230 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Apr 2020to 17 Apr 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Single PCX images collected from patients over 18 years of age
Who cannot join
- CT scans composed of multiple concerted X-rays
- Single PCX images collected from patients under 18 years of age
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Classification of COVID-19 Infection in Posteroanterior Chest X-rays With Common Deep Learning Architectures
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.
Read the 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
- Being tested
- CovX
- Keywords
- machine learning, X-ray, classification, COVID-19, posteroanterior, convolutional neural network architectures
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CovXDevice
Convolutional neural network for classification of COVID-19 from chest X-rays
| Study group | Role | What they receive |
|---|---|---|
| COVID-19 Patients | Single posteroanterior (or "front-on") X-rays collected from COVID-19 patients Receives: Device: CovX | |
| Non COVID-19 Patients | Single posteroanterior (or "front-on") X-rays collected from subsets of non COVID-19 patients Receives: Device: CovX |
What is being measured
The primary outcome is the main question the study is designed to answer.
Identification of COVID-19
Identification of COVID-19 infection from chest X-ray analysis
Measured: Through study completion, an average of 2 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Apr 2020
- Study start1 Apr 2020
- Primary completion17 Apr 2020
- Study completion17 Apr 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 230 participants (actual)
Sponsor and investigators
- Lead sponsor
- Dascena
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04358536
- Sponsor's protocol ID
- 04202002
- Status last verified
- Apr 2020
- First submitted
- 20 Apr 2020
- Last updated
- 24 Apr 2020
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 24 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Classification of COVID-19 Infection in Posteroanterior Chest X-rays With Common Deep Learning Architectures
- ClinicalTrials.gov ID
- NCT04358536
- Acronym / sponsor ID
- 04202002
- Conditions
- COVID-19
- Keywords
- machine learning, X-ray, classification, COVID-19, posteroanterior, convolutional neural network architectures
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 230 participants (actual)
- Lead sponsor
- DascenaIndustry (pharma, biotech, devices)
- Study start
- 1 Apr 2020 (actual)
- Primary completion
- 17 Apr 2020 (actual)
- Study completion
- 17 Apr 2020 (actual)
- First posted
- 24 Apr 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| COVID-19 Patients | – | Device: CovXSingle posteroanterior (or "front-on") X-rays collected from COVID-19 patients |
| Non COVID-19 Patients | – | Device: CovXSingle posteroanterior (or "front-on") X-rays collected from subsets of non COVID-19 patients |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Single PCX images collected from patients over 18 years of age
Key exclusion criteria (2 listed)
- CT scans composed of multiple concerted X-rays
- Single PCX images collected from patients under 18 years of age
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CovXConvolutional neural network for classification of COVID-19 from chest X-rays | Device | COVID-19 Patients, Non COVID-19 Patients |
Primary outcome
Identification of COVID-19
Time frame: Through study completion, an average of 2 months
Identification of COVID-19 infection from chest X-ray analysis
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Apr 2020.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID-19, run by Dascena at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Apr 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identification of COVID-19 (time frame: Through study completion, an average of 2 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Apr 2020.