COVID-19AI-COVID-XrNCT04313946
Artificial Intelligence Algorithms for Discriminating Between COVID-19 and Influenza Pneumonitis Using Chest X-Rays
An observational study of COVID-19 and Pneumonia, Viral, run by Professor Adrian Covic at 3 sites in 3 countries.
Trial summary
This project aims to use artificial intelligence (image discrimination) algorithms, specifically convolutional neural networks (CNNs) for scanning chest radiographs in the emergency department (triage) in patients with suspected respiratory symptoms (fever, cough, myalgia) of coronavirus infection COVID 19. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- COVID-19, Pneumonia, Viral, Influenza With Pneumonia
- What is being tested?
- Scanning Chest X-rays and performing AI algorithms on images
- How many people
- 200 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 18 Mar 2020to 18 Aug 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All patients with influenza symptoms that arrive at emergency department with cough, fever, myalgia - which are suspected of COVID-19 infection
Who may be able to join
- flu-like symptoms: myalgia, cough, fever, sputum
- Chest X-Rays
- COVID-19 biological tests
Who cannot join
- patient refusal
- uncertain radiographs
- uncertain tests results
Trial locations and contacts
3 sites in 3 countries.
3 sites in 3 countries
Italy1 site, 1 recruiting
- Recruiting
U.O. Multidisciplinare di Patologia Mammaria e Ricerca Traslazionale; Dipartimento Universitario Clinico di Scienze Mediche, Chirurgiche e della Salute Università degli Studi di Trieste
Cremona, 26100
Daniele Generali, MD, PhD, +390372408042, dgenerali@units.it
Daniele Generali, MD, PhD
Romania1 site, 1 recruiting
- Recruiting
University of Medicine and Pharmacy Gr T Popa
Iași, 700503
Alexandru Burlacu, MD, PhD, 0040744488580, alexandru.burlacu@umfiasi.ro
Alexandru Burlacu, MD, PhD
United Kingdom1 site, 1 recruiting
- Recruiting
Department of Cardiology at Chelsea and Westminster NHS hospital
London
Emmanuel Ako, MD, PhD, +447932970131, e.ako@ucl.ac.uk
Emmanuel Ako, MD, PhD
About this study
Official title: The Benefits of Artificial Intelligence Algorithms (CNNs) for Discriminating Between COVID-19 and Influenza Pneumonitis in an Emergency Department Using Chest X-Ray Examinations
This project aims to use artificial intelligence (image discrimination) algorithms, specifically convolutional neural networks (CNNs) for scanning chest radiographs in the emergency department (triage) in patients with suspected respiratory symptoms (fever, cough, myalgia) of coronavirus infection COVID 19. The objective is to create and validate a software solution that discriminates on the basis of the chest x-ray between Covid-19 pneumonitis and influenza
Read the detailed description
This project aims to use artificial intelligence (image discrimination) algorithms;
- specifically convolutional neural networks (CNNs) for scanning chest radiographs in the emergency department (triage) in patients with suspected respiratory symptoms (fever, cough, myalgia) of coronavirus infection COVID 19;
- the objective is to create and validate a software solution that discriminates on the basis of the chest x-ray between Covid-19 pneumonitis and influenza;
- this software will be trained by introducing X-Rays from patients with/without COVID-19 pneumonitis and/or flu pneumonitis;
- the same AI algorithm will run on future X-Ray scans for predicting possible COVID-19 pneumonitis
- Conditions
- COVID-19, Pneumonia, Viral, Influenza With Pneumonia, Flu Symptom, Flu Like Illness, Pneumonia, Interstitial
- Being tested
- Scanning Chest X-rays and performing AI algorithms on images
- Keywords
- Artificial Intelligence, CNNs, COVID-19, chest X-Ray, Emergency Department, Triage, Flu
- Medical subject headings
- COVID-19, Pneumonia, Viral, Lung Diseases, Interstitial, Pneumonia, Ventilator-Associated, Pneumonia, Mycoplasma, Emergencies, Influenza, Human
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Scanning Chest X-rays and performing AI algorithms on imagesDiagnostic test
Chest X-Rays; AI CNNs; Results
| Study group | Role | What they receive |
|---|---|---|
| Symptomatic Patients | Our goal is to identify an artificial intelligence algorithm that can be run on lung radiographs in patients with influenza / respiratory viral symptoms who come to the emergency department / triage. This algorithm aims to identify the radiographs of patients with COVID-19 and those with influenza pneumonitis, with accuracy verified by COVID-19 tests. Receives: Diagnostic Test: Scanning Chest X-rays and performing AI algorithms on images |
What is being measured
The primary outcome is the main question the study is designed to answer.
COVID-19 positive X-Rays
Number of participants with pneumonitis on Chest X-Ray and COVID 19 positive
Measured: 6 months
COVID-19 negative X-Rays
Number of participants with pneumonitis on Chest X-Ray and COVID 19 negative
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Mar 2020
- Study start18 Mar 2020
- Primary completion16 Aug 2020 (estimated)
- Study completion18 Aug 2020 (estimated)
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
- Ecologic or community
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Professor Adrian Covic
- Sponsor type
- University, hospital or other
- Collaborators
- Falcon Trading Iasi, Romanian Academy of Medical Sciences
- Responsible party
- Sponsor-investigator, Professor Adrian Covic, Grigore T. Popa University of Medicine and Pharmacy
- Investigators
- Alexandru Burlacu, Lecturer, Principal investigator, University of Medicine and Pharmacy Gr T Popa - IasiRadu Dabija, Lecturer, Principal investigator, University of Medicine and Pharmacy Gr T Popa - Iasi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04313946
- Sponsor's protocol ID
- 0110
- Acronym
- AI-COVID-Xr
- Status last verified
- Apr 2020
- First submitted
- 17 Mar 2020
- Last updated
- 27 Apr 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Yes, we would be happy to share the algorithm code and the results with any scientist interested (without any financial interests)
Study at a glance
From the registry record, last updated by the sponsor on 27 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Benefits of Artificial Intelligence Algorithms (CNNs) for Discriminating Between COVID-19 and Influenza Pneumonitis in an Emergency Department Using Chest X-Ray Examinations
- ClinicalTrials.gov ID
- NCT04313946
- Acronym / sponsor ID
- AI-COVID-Xr / 0110
- Conditions
- COVID-19, Pneumonia, Viral, Influenza With Pneumonia, Flu Symptom, Flu Like Illness, Pneumonia, Interstitial
- Keywords
- Artificial Intelligence, CNNs, COVID-19, chest X-Ray, Emergency Department, Triage, Flu
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Professor Adrian CovicUniversity, hospital or other
- Collaborators
- Falcon Trading Iasi, Romanian Academy of Medical Sciences
- Study start
- 18 Mar 2020 (actual)
- Primary completion
- 16 Aug 2020 (estimated)
- Study completion
- 18 Aug 2020 (estimated)
- First posted
- 18 Mar 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.
This project aims to use artificial intelligence (image discrimination) algorithms, specifically convolutional neural networks (CNNs) for scanning chest radiographs in the emergency department (triage) in patients with suspected respiratory symptoms (fever, cough, myalgia) of coronavirus infection COVID 19. The objective is to create and validate a software solution that discriminates on the basis of the chest x-ray between Covid-19 pneumonitis and influenza
Detailed description
This project aims to use artificial intelligence (image discrimination) algorithms;
- specifically convolutional neural networks (CNNs) for scanning chest radiographs in the emergency department (triage) in patients with suspected respiratory symptoms (fever, cough, myalgia) of coronavirus infection COVID 19;
- the objective is to create and validate a software solution that discriminates on the basis of the chest x-ray between Covid-19 pneumonitis and influenza;
- this software will be trained by introducing X-Rays from patients with/without COVID-19 pneumonitis and/or flu pneumonitis;
- the same AI algorithm will run on future X-Ray scans for predicting possible COVID-19 pneumonitis
Study design
- Observational model
- Ecologic or community
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Symptomatic Patients | – | Diagnostic Test: Scanning Chest X-rays and performing AI algorithms on imagesOur goal is to identify an artificial intelligence algorithm that can be run on lung radiographs in patients with influenza / respiratory viral symptoms who come to the emergency department / triage. This algorithm aims to identify the radiographs of patients with COVID-19 and those with influenza pneumonitis, with accuracy verified by COVID-19 tests. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patients with influenza symptoms that arrive at emergency department with cough, fever, myalgia - which are suspected of COVID-19 infection
Key inclusion criteria (3 listed)
- flu-like symptoms: myalgia, cough, fever, sputum
- Chest X-Rays
- COVID-19 biological tests
Key exclusion criteria (3 listed)
- patient refusal
- uncertain radiographs
- uncertain tests results
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 |
|---|---|---|
| Scanning Chest X-rays and performing AI algorithms on imagesChest X-Rays; AI CNNs; Results | Diagnostic test | Symptomatic Patients |
Primary outcome
COVID-19 positive X-Rays
Time frame: 6 months
Number of participants with pneumonitis on Chest X-Ray and COVID 19 positive
COVID-19 negative X-Rays
Time frame: 6 months
Number of participants with pneumonitis on Chest X-Ray and COVID 19 negative
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Apr 2020.
- Individual participant dataSharing planned: Yes. Yes, we would be happy to share the algorithm code and the results with any scientist interested (without any financial interests)
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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.
Alexandru Burlacu, Lecturer principal investigator
University of Medicine and Pharmacy Gr T Popa - Iasi
Radu Dabija, Lecturer principal investigator
University of Medicine and Pharmacy Gr T Popa - Iasi
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID-19 and Pneumonia, Viral, run by Professor Adrian Covic at 3 sites in 3 countries.
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 3 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: status not confirmed, as of the sponsor's last update on 27 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 3 sites in Italy, Romania, United Kingdom. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Alexandru Burlacu, MD, PhD (0040744488580, alexandru.burlacu@umfiasi.ro), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
COVID-19 positive X-Rays (time frame: 6 months). The study lists 2 primary outcomes in all. 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 27 Apr 2020.