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

• Sponsor: Professor Adrian Covic (Other)
• Overall Status: Unknown status
• CT.gov ID: NCT04313946
• Collaborator: Falcon Trading Iasi (Other), Romanian Academy of Medical Sciences (Other)
• Enrollment: 200
• Locations: 3
• Anticipated Duration (Months): 5
• Patients Per Site: 66.7
• Patients Per Site Per Month: 13.3

Study Details

Study Description

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	Phase
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

• Study Type: Observational
• Anticipated 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: Mar 18, 2020
• Anticipated Primary Completion Date: Aug 16, 2020
• Anticipated Study Completion Date: Aug 18, 2020

Arms and Interventions

Arm	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

Primary Outcome Measures

1. COVID-19 positive X-Rays [6 months]

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

2. COVID-19 negative X-Rays [6 months]

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

Eligibility Criteria

Criteria

• Ages Eligible for Study: N/A and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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

Locations

	Site	City	State	Country	Postal Code
1	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		Italy	26100
2	University of Medicine and Pharmacy Gr T Popa	Iaşi		Romania	700503
3	Department of Cardiology at Chelsea and Westminster NHS hospital	London		United Kingdom

Sponsors and Collaborators

• Professor Adrian Covic
• 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 Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• 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: Mar 18, 2020
• Last Update Posted: Apr 27, 2020
• Last Verified: Apr 1, 2020

• Individual Participant Data (IPD) Sharing Statement: Yes
• Plan to Share IPD: Yes
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Professor Adrian Covic, Clinical Professor, Grigore T. Popa University of Medicine and Pharmacy: Artificial Intelligence; CNNs; COVID-19; chest X-Ray; Emergency Department; Triage; Flu
• Additional relevant MeSH terms: COVID-19; Influenza, Human; Pneumonia; Pneumonia, Viral; Pneumonia, Ventilator-Associated; Lung Diseases, Interstitial

Study Results

No Results Posted as of Apr 27, 2020