We're building a better ClinicalTrials.gov. Check it out and tell us what you think!
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

Artificial Intelligence Algorithms for Discriminating Between COVID-19 and Influenza Pneumonitis Using Chest X-Rays (AI-COVID-Xr)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04313946
Recruitment Status : Unknown
Verified April 2020 by Professor Adrian Covic, Grigore T. Popa University of Medicine and Pharmacy.
Recruitment status was:  Recruiting
First Posted : March 18, 2020
Last Update Posted : April 27, 2020
Sponsor:
Collaborators:
Falcon Trading Iasi
Romanian Academy of Medical Sciences
Information provided by (Responsible Party):
Professor Adrian Covic, Grigore T. Popa University of Medicine and Pharmacy

Study Description
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
Brief 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. 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

Condition or disease Intervention/treatment
COVID-19 Pneumonia, Viral Influenza With Pneumonia Flu Symptom Flu Like Illness Pneumonia, Interstitial Pneumonia, Ventilator-Associated Pneumonia Atypical Diagnostic Test: Scanning Chest X-rays and performing AI algorithms on images

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
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Layout table for study information
Study Type : Observational
Estimated Enrollment : 200 participants
Observational Model: Ecologic or Community
Time Perspective: Prospective
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
Actual Study Start Date : March 18, 2020
Estimated Primary Completion Date : August 16, 2020
Estimated Study Completion Date : August 18, 2020

Resource links provided by the National Library of Medicine

Drug Information available for: X-Rays

Groups and Cohorts
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

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.
 Diagnostic Test: Scanning Chest X-rays and performing AI algorithms on images
Chest X-Rays; AI CNNs; Results


Outcome Measures
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Primary Outcome Measures :
  1. COVID-19 positive X-Rays [ Time Frame: 6 months ]
    Number of participants with pneumonitis on Chest X-Ray and COVID 19 positive

  2. COVID-19 negative X-Rays [ Time Frame: 6 months ]
    Number of participants with pneumonitis on Chest X-Ray and COVID 19 negative


Eligibility Criteria
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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
Criteria

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
Contacts and Locations
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04313946


Contacts
Layout table for location contacts
Contact: Alexandru Burlacu, MD, PhD 0040744488580 alexandru.burlacu@umfiasi.ro

Locations
Layout table for location information
Italy
U.O. Multidisciplinare di Patologia Mammaria e Ricerca Traslazionale; Dipartimento Universitario Clinico di Scienze Mediche, Chirurgiche e della Salute Università degli Studi di Trieste Recruiting
Cremona, Italy, 26100
Contact: Daniele Generali, MD, PhD    +390372408042    dgenerali@units.it   
Principal Investigator: Daniele Generali, MD, PhD         
Romania
University of Medicine and Pharmacy Gr T Popa Recruiting
Iaşi, Romania, 700503
Contact: Alexandru Burlacu, MD, PhD    0040744488580    alexandru.burlacu@umfiasi.ro   
Principal Investigator: Alexandru Burlacu, MD, PhD         
United Kingdom
Department of Cardiology at Chelsea and Westminster NHS hospital Recruiting
London, United Kingdom
Contact: Emmanuel Ako, MD, PhD    +447932970131    e.ako@ucl.ac.uk   
Principal Investigator: Emmanuel Ako, MD, PhD         
Sponsors and Collaborators
Professor Adrian Covic
Falcon Trading Iasi
Romanian Academy of Medical Sciences
Investigators
Layout table for investigator information
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
More Information
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
Layout table for additonal information
Responsible Party: Professor Adrian Covic, Clinical Professor, Grigore T. Popa University of Medicine and Pharmacy
ClinicalTrials.gov Identifier: NCT04313946    
Other Study ID Numbers: 0110
First Posted: March 18, 2020    Key Record Dates
Last Update Posted: April 27, 2020
Last Verified: April 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: Yes, we would be happy to share the algorithm code and the results with any scientist interested (without any financial interests)
Supporting Materials: Study Protocol
Informed Consent Form (ICF)
Analytic Code

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Professor Adrian Covic, Grigore T. Popa University of Medicine and Pharmacy:
Artificial Intelligence
CNNs
COVID-19
chest X-Ray
 Emergency Department
Triage
Flu
Additional relevant MeSH terms:
Layout table for MeSH terms
COVID-19
Influenza, Human
Pneumonia
Pneumonia, Viral
Pneumonia, Ventilator-Associated
Lung Diseases, Interstitial
Respiratory Tract Infections
Infections
Virus Diseases
Coronavirus Infections
Coronaviridae Infections
 Nidovirales Infections
RNA Virus Infections
Lung Diseases
Respiratory Tract Diseases
Orthomyxoviridae Infections
Healthcare-Associated Pneumonia
Cross Infection
Iatrogenic Disease
Disease Attributes
Pathologic Processes