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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

3599 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-08-26

Study Completion Date

2022-11-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Automated Quantification of Radiologic Pulmonary Alteration During Acute Respiratory Failure: Application to the COVID-19 Pandemic

NCT05278390

SARS-CoV-2 Infections Respiratory Failure With Hypoxia

UNKNOWN NA

Pulmonary Disease in Patients Referred for Coronary CT

NCT01734629

Coronary Artery Disease Atheroscleroses COPD +1 more

COMPLETED

Low-dose CT for Diagnosis of Pneumonia in COPD Exacerbations and Comparison of the Inflammatory Profile.

NCT02264483

COPD Exacerbation

UNKNOWN

Study of Lung Proteins in Patients With Pneumonia

NCT00077909

Pneumonia Pulmonary Disease Lung Disease

RECRUITING

Evaluation of Novel Lung Function Parameters and Quantitative Computed Tomography (qCT) in Patients With Pulmonary Disease

NCT02826265

Chronic Obstructive Pulmonary Disease Bronchial Asthma Interstitial Lung Disease

UNKNOWN

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

COVID-19 (Coronavirus Disease 2019) COVID-19 Pneumonia

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Normal chest radiographs

X-rays without alterations in the lung parenchyma

Categorization of chest xrays images

Intervention Type OTHER

Use of Convolutional Neural Network Model to categorize chest xrays images in each group.

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.

Categorization of chest xrays images

Intervention Type OTHER

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

Categorization of chest xrays images

Intervention Type OTHER

Use of Convolutional Neural Network Model to categorize chest xrays images in each group.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Categorization of chest xrays images

Use of Convolutional Neural Network Model to categorize chest xrays images in each group.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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)