COVID-19 (Coronavirus Disease 2019)RedNeumonNCT05722665
Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs
An observational study of COVID-19 (Coronavirus Disease 2019) and COVID-19 Pneumonia, run by Fundacion Clinica Valle del Lili at 1 site in Colombia.
Trial summary
This study aims to design a Convolutional Neural Network (CNN) and apply an attention model to help differentiate pneumonia due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), pneumonia due to other viruses/bacteria, and normal chest x-ray (CXR) in clinical practice. 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 (Coronavirus Disease 2019), COVID-19 Pneumonia
- What is being tested?
- Categorization of chest xrays images
- How many people
- 3,599 participants
- Where
- 1 siteColombia
- Study dates
- 26 Aug 2021to 30 Nov 2022
- 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: Group 1: X-rays without alterations in the lung parenchyma Group 2: X-rays belonging to patients with a diagnosis of pneumonia other than COVID-19 Group 3: X-rays belonging to patients with a diagnosis of COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.
Who may be able to join
- Chest radiographs from patients without COVID-19 or other pneumonia took before the pandemic start date (January 2020)
- Chest radiographs from patients with COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.
- Chest radiographs from patients without COVID-19 confirmed by a negative Reverse Transcriptase polymerase chain reaction (RT-PCR) and other pneumonia diagnoses taken before the pandemic start date (January 2020)
Who cannot join
- N/A
Trial locations and contacts
1 site in 1 country.
Colombia1 site
Fundacion Valle del Lili
Cali, Valle del Cauca Department, 760001
About this study
Official title: The Predictive Capacity of a Convolutional Neural Network (CNN) Model to Detect Viral Pneumonia in Adult Patients With Coronavirus Disease 2019 (COVID-19) in Cali, Colombia
This study aims to design a Convolutional Neural Network (CNN) and apply an attention model to help differentiate pneumonia due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), pneumonia due to other viruses/bacteria, and normal chest x-ray (CXR) in clinical practice. A bank of digital chest images from a high-complexity health facility in Cali, Colombia, was used.
Read the detailed description
To differentiate coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia, expert radiologists must analyze the chest x-ray (CXR) to identify visual, radiographic patterns associated with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. It is challenging because the findings are similar for different types of pneumonia.
Since the manual diagnosis of COVID-19 from CXR is a difficult and time-consuming process, applying deep learning (DL) models to medical image analysis is a current hot research topic. This work will develop a new Convolutional Neural Network (CNN) to detect COVID-19 radiographs. It will use a large dataset of chest radiographs classified into three classes: viral/bacterial pneumonia, COVID-19 pneumonia, and normal images. The study aims to incorporate a new attention module that applies CNNs to the linear projection operation to help differentiate COVID-19 pneumonia from other pneumonia and normal chest radiographs in clinical practice.
- Being tested
- Categorization of chest xrays images
- Keywords
- COVID-19 (coronavirus disease 2019), Diagnostic Imaging, Radiography, Thorax, Computer Neural Network, Deep Learning
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Categorization of chest xrays imagesOther
Use of Convolutional Neural Network Model to categorize chest xrays images in each group.
| Study group | Role | What they receive |
|---|---|---|
| Normal chest radiographs | X-rays without alterations in the lung parenchyma Receives: Other: Categorization of chest xrays images | |
| COVID-19 chest radiographs | X-rays belonging to patients with a diagnosis of COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen. Receives: Other: Categorization of chest xrays images | |
| Other pneumonia chest radiographs | X-rays belonging to patients with a diagnosis of pneumonia other than COVID-19 Receives: Other: Categorization of chest xrays images |
What is being measured
The primary outcome is the main question the study is designed to answer.
COVID-19 (coronavirus disease 2019) pneumonia chest radiograph identified
Development and determination of the predictive capacity of a Convolutional Neural Network model to detect viral pneumonia in chest radiographs of adult patients with acute respiratory disease secondary to SARS-COV-2 infection.
Measured: month 8
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Feb 2023
- Study start26 Aug 2021
- Primary completion30 Nov 2022
- Study completion30 Nov 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19 (Coronavirus Disease 2019)Studies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 3,599 participants (actual)
Sponsor and investigators
- Lead sponsor
- Fundacion Clinica Valle del Lili
- Sponsor type
- University, hospital or other
- Collaborators
- Universidad Autonoma de Occidente
- Responsible party
- Sponsor
- Investigators
- Liliana Fernandez, M.D, Principal investigator, Fundacion Clinica Valle del Lili
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05722665
- Sponsor's protocol ID
- 1805
- Acronym
- RedNeumon
- Status last verified
- Feb 2023
- First submitted
- 8 Feb 2023
- Last updated
- 23 Feb 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Predictive Capacity of a Convolutional Neural Network (CNN) Model to Detect Viral Pneumonia in Adult Patients With Coronavirus Disease 2019 (COVID-19) in Cali, Colombia
- ClinicalTrials.gov ID
- NCT05722665
- Acronym / sponsor ID
- RedNeumon / 1805
- Keywords
- COVID-19 (coronavirus disease 2019), Diagnostic Imaging, Radiography, Thorax, Computer Neural Network, Deep Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 3,599 participants (actual)
- Lead sponsor
- Fundacion Clinica Valle del LiliUniversity, hospital or other
- Collaborators
- Universidad Autonoma de Occidente
- Study start
- 26 Aug 2021 (actual)
- Primary completion
- 30 Nov 2022 (actual)
- Study completion
- 30 Nov 2022 (actual)
- First posted
- 10 Feb 2023 (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.
This study aims to design a Convolutional Neural Network (CNN) and apply an attention model to help differentiate pneumonia due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), pneumonia due to other viruses/bacteria, and normal chest x-ray (CXR) in clinical practice. A bank of digital chest images from a high-complexity health facility in Cali, Colombia, was used.
Detailed description
To differentiate coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia, expert radiologists must analyze the chest x-ray (CXR) to identify visual, radiographic patterns associated with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. It is challenging because the findings are similar for different types of pneumonia.
Since the manual diagnosis of COVID-19 from CXR is a difficult and time-consuming process, applying deep learning (DL) models to medical image analysis is a current hot research topic. This work will develop a new Convolutional Neural Network (CNN) to detect COVID-19 radiographs. It will use a large dataset of chest radiographs classified into three classes: viral/bacterial pneumonia, COVID-19 pneumonia, and normal images. The study aims to incorporate a new attention module that applies CNNs to the linear projection operation to help differentiate COVID-19 pneumonia from other pneumonia and normal chest radiographs in clinical practice.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Normal chest radiographs | – | Other: Categorization of chest xrays imagesX-rays without alterations in the lung parenchyma |
| COVID-19 chest radiographs | – | Other: Categorization of chest xrays imagesX-rays belonging to patients with a diagnosis of COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen. |
| Other pneumonia chest radiographs | – | Other: Categorization of chest xrays imagesX-rays belonging to patients with a diagnosis of pneumonia other than COVID-19 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Group 1: X-rays without alterations in the lung parenchyma Group 2: X-rays belonging to patients with a diagnosis of pneumonia other than COVID-19 Group 3: X-rays belonging to patients with a diagnosis of COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.
Key inclusion criteria (3 listed)
- Chest radiographs from patients without COVID-19 or other pneumonia took before the pandemic start date (January 2020)
- Chest radiographs from patients with COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.
- Chest radiographs from patients without COVID-19 confirmed by a negative Reverse Transcriptase polymerase chain reaction (RT-PCR) and other pneumonia diagnoses taken before the pandemic start date (January 2020)
Key exclusion criteria (1 listed)
- N/A
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 |
|---|---|---|
| Categorization of chest xrays imagesUse of Convolutional Neural Network Model to categorize chest xrays images in each group. | Other | COVID-19 chest radiographs, Normal chest radiographs, Other pneumonia chest radiographs |
Primary outcome
COVID-19 (coronavirus disease 2019) pneumonia chest radiograph identified
Time frame: month 8
Development and determination of the predictive capacity of a Convolutional Neural Network model to detect viral pneumonia in chest radiographs of adult patients with acute respiratory disease secondary to SARS-COV-2 infection.
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 23 Feb 2023.
- Individual participant dataSharing planned: No
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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.
Liliana Fernandez, M.D principal investigator
Fundacion Clinica Valle del Lili
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 (Coronavirus Disease 2019) and COVID-19 Pneumonia, run by Fundacion Clinica Valle del Lili at 1 site in Colombia.
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 3 inclusion and 1 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 23 Feb 2023. 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 Colombia. 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?
COVID-19 (coronavirus disease 2019) pneumonia chest radiograph identified (time frame: month 8). 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 23 Feb 2023.