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

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ClinicalTrials.gov Identifier: NCT04313946
Recruitment Status : 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

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

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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

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




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



Information from the National Library of Medicine

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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

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
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Contact: Alexandru Burlacu, MD, PhD 0040744488580 alexandru.burlacu@umfiasi.ro

Locations
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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
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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
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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

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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:
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Pneumonia, Ventilator-Associated
Influenza, Human
Pneumonia, Viral
Pneumonia
Lung Diseases, Interstitial
Orthomyxoviridae Infections
RNA Virus Infections
Virus Diseases
Respiratory Tract Infections
Respiratory Tract Diseases
Lung Diseases
Cross Infection
Infection