Clinical trial NCT04313946: Artificial Intelligence Algorithms for Discriminating Between COVID-19 and Influenza Pneumonitis Using Chest X-Rays
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
Study type: OBSERVATIONAL
Overall status: UNKNOWN
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
Lead sponsor: Grigore T. Popa University of Medicine and Pharmacy
Sponsor class: OTHER
Collaborators: name: Falcon Trading Iasi
name: Romanian Academy of Medical Sciences
First posted: 2020-03-18
Enrollment: 200
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: ECOLOGIC_OR_COMMUNITY
Time perspective: PROSPECTIVE
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

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

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

Eligibility 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

Primary outcomes:
measure: COVID-19 positive X-Rays
timeFrame: 6 months
measure: COVID-19 negative X-Rays
timeFrame: 6 months
Secondary outcomes:
Other outcomes:
Locations:
city: Cremona
country: Italy
facility: U.O. Multidisciplinare di Patologia Mammaria e Ricerca Traslazionale; Dipartimento Universitario Clinico di Scienze Mediche, Chirurgiche e della Salute Università degli Studi di Trieste
city: Iaşi
country: Romania
facility: University of Medicine and Pharmacy Gr T Popa
city: London
country: United Kingdom
facility: Department of Cardiology at Chelsea and Westminster NHS hospital
Study officials:
affiliation: University of Medicine and Pharmacy Gr T Popa - Iasi
name: Alexandru Burlacu, Lecturer
role: PRINCIPAL_INVESTIGATOR
affiliation: University of Medicine and Pharmacy Gr T Popa - Iasi
name: Radu Dabija, Lecturer
role: PRINCIPAL_INVESTIGATOR
References: