Clinical trial NCT05722665: Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID-19 (Coronavirus Disease 2019)
COVID-19 Pneumonia
Keywords: COVID-19 (coronavirus disease 2019)
Diagnostic Imaging
Radiography
Thorax
Computer Neural Network
Deep Learning
Lead sponsor: Fundacion Clinica Valle del Lili
Sponsor class: OTHER
Collaborators: name: Universidad Autonoma de Occidente
First posted: 2023-02-10
Enrollment: 3599
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: RETROSPECTIVE
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.

Brief 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: COVID-19 (coronavirus disease 2019) pneumonia chest radiograph identified
timeFrame: month 8
Secondary outcomes:
Other outcomes:
Locations:
city: Cali
country: Colombia
facility: Fundacion Valle del Lili
state: Valle Del Cauca
Study officials:
affiliation: Fundacion Clinica Valle del Lili
name: Liliana Fernandez, M.D
role: PRINCIPAL_INVESTIGATOR
References: