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

COVID-19 Pneumonia Clinical Trial

— RedNeumon  

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05722665
• Other study ID #: 1805
• Status: Completed
• Start date: August 26, 2021
• Est. completion date: November 30, 2022

Study information

• Verified date: February 2023
• Source: Fundacion Clinica Valle del Lili
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 3599
• Est. completion date: November 30, 2022
• Est. primary completion date: November 30, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Coronavirus Infections
• COVID-19
• COVID-19 Pneumonia
• Pneumonia

Intervention

Other:
• Categorization of chest xrays images
Use of Convolutional Neural Network Model to categorize chest xrays images in each group.

Locations

Country	Name	City	State
Colombia	Fundacion Valle del Lili	Cali	Valle Del Cauca

Sponsors (2)

Lead Sponsor	Collaborator
Fundacion Clinica Valle del Lili	Universidad Autonoma de Occidente

Country where clinical trial is conducted

Colombia, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	COVID-19 (coronavirus disease 2019) pneumonia chest radiograph identified	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