Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs (RedNeumon)

February 20, 2023 updated by: Fundacion Clinica Valle del Lili

The Predictive Capacity of a Convolutional Neural Network (CNN) Model to Detect Viral Pneumonia in Adult Patients With Coronavirus Disease 2019 (COVID-19) in Cali, Colombia

This study aims to design a Convolutional Neural Network (CNN) and apply an attention model to help differentiate pneumonia due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), pneumonia due to other viruses/bacteria, and normal chest x-ray (CXR) in clinical practice. A bank of digital chest images from a high-complexity health facility in Cali, Colombia, was used.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

To differentiate coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia, expert radiologists must analyze the chest x-ray (CXR) to identify visual, radiographic patterns associated with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. It is challenging because the findings are similar for different types of pneumonia.

Since the manual diagnosis of COVID-19 from CXR is a difficult and time-consuming process, applying deep learning (DL) models to medical image analysis is a current hot research topic. This work will develop a new Convolutional Neural Network (CNN) to detect COVID-19 radiographs. It will use a large dataset of chest radiographs classified into three classes: viral/bacterial pneumonia, COVID-19 pneumonia, and normal images. The study aims to incorporate a new attention module that applies CNNs to the linear projection operation to help differentiate COVID-19 pneumonia from other pneumonia and normal chest radiographs in clinical practice.

Study Type

Observational

Enrollment (Actual)

3599

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Colombia
    • Valle Del Cauca
      • Cali, Valle Del Cauca, Colombia, 760001
        • Fundacion Valle del Lili

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Group 1: X-rays without alterations in the lung parenchyma Group 2: X-rays belonging to patients with a diagnosis of pneumonia other than COVID-19 Group 3: X-rays belonging to patients with a diagnosis of COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.

Description

Inclusion Criteria:

  • Chest radiographs from patients without COVID-19 or other pneumonia took before the pandemic start date (January 2020)
  • Chest radiographs from patients with COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.
  • Chest radiographs from patients without COVID-19 confirmed by a negative Reverse Transcriptase polymerase chain reaction (RT-PCR) and other pneumonia diagnoses taken before the pandemic start date (January 2020)

Exclusion Criteria:

  • N/A

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
COVID-19 chest radiographs
X-rays belonging to patients with a diagnosis of COVID-19 confirmed by positive Reverse Transcriptase polymerase chain reaction (RT-PCR) and/or presence of antibodies to COVID-19 and/or positive COVID-19 viral antigen.
Other: Categorization of chest xrays images
Use of Convolutional Neural Network Model to categorize chest xrays images in each group.
Other pneumonia chest radiographs
X-rays belonging to patients with a diagnosis of pneumonia other than COVID-19
Other: Categorization of chest xrays images
Use of Convolutional Neural Network Model to categorize chest xrays images in each group.
Normal chest radiographs
X-rays without alterations in the lung parenchyma
Other: Categorization of chest xrays images
Use of Convolutional Neural Network Model to categorize chest xrays images in each group.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
COVID-19 (coronavirus disease 2019) pneumonia chest radiograph identified
Time Frame: month 8
Development and determination of the predictive capacity of a Convolutional Neural Network model to detect viral pneumonia in chest radiographs of adult patients with acute respiratory disease secondary to SARS-COV-2 infection.
month 8

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fundacion Clinica Valle del Lili

Collaborators

Universidad Autonoma de Occidente

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 26, 2021

Primary Completion (Actual)

November 30, 2022

Study Completion (Actual)

November 30, 2022

Study Registration Dates

First Submitted

February 8, 2023

First Submitted That Met QC Criteria

February 8, 2023

First Posted (Actual)

February 10, 2023

Study Record Updates

Last Update Posted (Estimate)

February 23, 2023

Last Update Submitted That Met QC Criteria

February 20, 2023

Last Verified

February 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 1805

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

We do not plan to share IPD, since we are not allowed to share information concerning the medical history of our patients or health workers.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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