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

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: COVID-19; Pneumonia, Viral; Pneumonia, Ventilator-Associated; Flu Symptom; Flu Like Illness; Influenza With Pneumonia; Pneumonia, Interstitial; Pneumonia Atypical
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

Study Locations

• Italy
  • 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
    • Contact: Daniele Generali, MD, PhD; Phone Number: +390372408042; Email: dgenerali@units.it
    • Principal Investigator: Daniele Generali, MD, PhD
• Romania
  • Iaşi, Romania, 700503
    • Recruiting
    • University of Medicine and Pharmacy Gr T Popa
    • Contact: Alexandru Burlacu, MD, PhD; Phone Number: 0040744488580; Email: alexandru.burlacu@umfiasi.ro
    • Principal Investigator: Alexandru Burlacu, MD, PhD
• United Kingdom
  • London, United Kingdom
    • Recruiting
    • Department of Cardiology at Chelsea and Westminster NHS hospital
    • Contact: Emmanuel Ako, MD, PhD; Phone Number: +447932970131; Email: e.ako@ucl.ac.uk
    • Principal Investigator: Emmanuel Ako, MD, PhD

Participation Criteria

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

How is the study designed?

Number of groups / cohorts

1

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Professor Adrian Covic
• Collaborators: Falcon Trading Iasi; Romanian Academy of Medical Sciences
• 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

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

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