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

NCT04313946 | Unknown DMC

• 1 other identifier: 0110
• Study Type: observational
• Target: 200
• Locations: 3 countries
• Sites: 3

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

Conditions

COVID-19; Pneumonia, Viral; Influenza With Pneumonia; Flu Symptom; Flu Like Illness; Pneumonia, Interstitial; Pneumonia, Ventilator-Associated; Pneumonia Atypical

Keywords

Artificial Intelligence; CNNs; COVID-19; chest X-Ray; Emergency Department; Triage; Flu

Outcome Measures

Primary Outcomes (2)

• COVID-19 positive X-Rays

  Number of participants with pneumonitis on Chest X-Ray and COVID 19 positive

  6 months

• COVID-19 negative X-Rays

  Number of participants with pneumonitis on Chest X-Ray and COVID 19 negative

  6 months

Study Arms (1)

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

Interventions

Scanning Chest X-rays and performing AI algorithms on images DIAGNOSTIC_TEST

Chest X-Rays; AI CNNs; Results

Symptomatic Patients

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

• flu-like symptoms: myalgia, cough, fever, sputum
• Chest X-Rays
• COVID-19 biological tests

You may not qualify if:

• patient refusal
• uncertain radiographs
• uncertain tests results

Sponsors & Collaborators

• Professor Adrian Covic: lead
• Falcon Trading Iasi: collaborator
• Romanian Academy of Medical Sciences: collaborator

Study Sites (3)

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, Italy

RECRUITING

University of Medicine and Pharmacy Gr T Popa

Iași, 700503, Romania

RECRUITING

Department of Cardiology at Chelsea and Westminster NHS hospital

London, United Kingdom

RECRUITING

MeSH Terms

Conditions

COVID-19; Pneumonia, Viral; Lung Diseases, Interstitial; Pneumonia, Ventilator-Associated; Pneumonia, Mycoplasma; Emergencies; Influenza, Human

Condition Hierarchy (Ancestors)

Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases; Healthcare-Associated Pneumonia; Cross Infection; Iatrogenic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Mycoplasma Infections; Mycoplasmatales Infections; Gram-Negative Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Pneumonia, Bacterial; Orthomyxoviridae Infections

Study Officials

• 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

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alexandru Burlacu, MD, PhD

CONTACT

0040744488580; alexandru.burlacu@umfiasi.ro

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: March 17, 2020
• First Posted: March 18, 2020
• Study Start: March 18, 2020
• Primary Completion: August 16, 2020
• Study Completion: August 18, 2020
• Last Updated: April 27, 2020

Record last verified: 2020-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF, ANALYTIC CODE

Locations

RO (1); GB (1); IT (1)