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| Name | Class |
|---|---|
| Universidad Autonoma de Occidente | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Normal chest radiographs | X-rays without alterations in the lung parenchyma |
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| 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 pneumonia chest radiographs | X-rays belonging to patients with a diagnosis of pneumonia other than COVID-19 |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Categorization of chest xrays images | Other | Use of Convolutional Neural Network Model to categorize chest xrays images in each group. |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Liliana Fernandez, M.D | Fundacion Clinica Valle del Lili | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fundacion Valle del Lili | Cali | Valle del Cauca Department | 760001 | Colombia |
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| ID | Term |
|---|---|
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
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| D014777 |
| Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |