- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04313946
Artificial Intelligence Algorithms for Discriminating Between COVID-19 and Influenza Pneumonitis Using Chest X-Rays (AI-COVID-Xr)
April 23, 2020 updated by: Professor Adrian Covic
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
Study Overview
Status
Unknown
Conditions
Intervention / Treatment
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 Type
Observational
Enrollment (Anticipated)
200
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Alexandru Burlacu, MD, PhD
- Phone Number: 0040744488580
- Email: alexandru.burlacu@umfiasi.ro
Study Locations
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Cremona, Italy, 26100
- 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
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Contact:
- Daniele Generali, MD, PhD
- Phone Number: +390372408042
- Email: dgenerali@units.it
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Principal Investigator:
- Daniele Generali, MD, PhD
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Iaşi, Romania, 700503
- Recruiting
- University of Medicine and Pharmacy Gr T Popa
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Contact:
- Alexandru Burlacu, MD, PhD
- Phone Number: 0040744488580
- Email: alexandru.burlacu@umfiasi.ro
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Principal Investigator:
- Alexandru Burlacu, MD, PhD
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London, United Kingdom
- Recruiting
- Department of Cardiology at Chelsea and Westminster NHS hospital
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Contact:
- Emmanuel Ako, MD, PhD
- Phone Number: +447932970131
- Email: e.ako@ucl.ac.uk
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Principal Investigator:
- Emmanuel Ako, MD, PhD
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Sampling Method
Probability Sample
Study Population
All patients with influenza symptoms that arrive at emergency department with cough, fever, myalgia - which are suspected of COVID-19 infection
Description
Inclusion Criteria:
- flu-like symptoms: myalgia, cough, fever, sputum
- Chest X-Rays
- COVID-19 biological tests
Exclusion Criteria:
- patient refusal
- uncertain radiographs
- uncertain tests results
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
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.
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Chest X-Rays; AI CNNs; Results
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
COVID-19 positive X-Rays
Time Frame: 6 months
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Number of participants with pneumonitis on Chest X-Ray and COVID 19 positive
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6 months
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COVID-19 negative X-Rays
Time Frame: 6 months
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Number of participants with pneumonitis on Chest X-Ray and COVID 19 negative
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6 months
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Investigators
- Principal Investigator: Alexandru Burlacu, Lecturer, University of Medicine and Pharmacy Gr T Popa - Iasi
- Principal Investigator: Radu Dabija, Lecturer, University of Medicine and Pharmacy Gr T Popa - Iasi
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
March 18, 2020
Primary Completion (Anticipated)
August 16, 2020
Study Completion (Anticipated)
August 18, 2020
Study Registration Dates
First Submitted
March 17, 2020
First Submitted That Met QC Criteria
March 17, 2020
First Posted (Actual)
March 18, 2020
Study Record Updates
Last Update Posted (Actual)
April 27, 2020
Last Update Submitted That Met QC Criteria
April 23, 2020
Last Verified
April 1, 2020
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Pathologic Processes
- Coronavirus Infections
- Coronaviridae Infections
- Nidovirales Infections
- RNA Virus Infections
- Virus Diseases
- Infections
- Respiratory Tract Infections
- Respiratory Tract Diseases
- Lung Diseases
- Disease Attributes
- Cross Infection
- Iatrogenic Disease
- Orthomyxoviridae Infections
- Healthcare-Associated Pneumonia
- COVID-19
- Pneumonia
- Influenza, Human
- Pneumonia, Viral
- Lung Diseases, Interstitial
- Pneumonia, Ventilator-Associated
Other Study ID Numbers
- 0110
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
YES
IPD Plan Description
Yes, we would be happy to share the algorithm code and the results with any scientist interested (without any financial interests)
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
- ICF
- ANALYTIC_CODE
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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