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COVID-19 Detection from Chest X-Rays using Deep Learning

Population Size

Not reported
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Years

2020 - 2021

Years statistic card

Associated BioSamples

None/not available

Associated BioSamples statistic card

Geographic coverage

Pakistan

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Lead time

Not applicable

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Summary

We aim to establish a reliable diagnostic tool based on a deep-learning framework for the screening of patients who present with COVID-19 related abnormalities on chest x-rays.

Documentation

COVID-19 is a pandemic having devastating implications on healthcare systems globally. Evidence shows that COVID-19 infected patients with pneumonia may present on chest x-rays with a pattern that is difficult to characterise using only the human eye. Therefore, artificial intelligence (AI) techniques using deep learning, which can consistently identify infected patients from non-infected ones given a radiographic examination of the patient, can be used as a reliable diagnostic tool. Considering chest x-rays are one of the most commonly performed radiological studies (coupled with the near universal availability of testing machines), applying AI techniques on them could prove to be valuable for COVID-19 diagnosis during clinical management. We therefore aim to establish a reliable diagnostic tool based on a deep-learning framework for the screening of patients who present with COVID-19 related abnormalities on chest x-rays. Over the course of 7 months we will build a dataset using open source data which are freely available, as well as with de-identified patient data collected from health institutions in Pakistan. Using this dataset, a deep learning model will be trained, which would be able to accurately screen patients who present with abnormalities relevant to COVID-19 in their radiographic examination. This tool will ultimately aid in expediting the diagnosis and referral of COVID-19 patients, resulting in improved clinical outcomes.

For further information, see: https://www.ed.ac.uk/usher/respire/covid-19/covid-19-detection-chest-x-rays

Dataset type

Health and disease

Dataset sub-type

Not applicable

Keywords

Observations

Observed Node

Disambiguating Description

Measured Value

Measured Property

Observation Date

Findings

1

Count

31 Jan 2021

Provenance

Image contrast

Not stated

Biological sample availability

None/not available

Details

Publishing frequency

Static

Version

2.0.0

Modified

08/10/2024

Citation Requirements

RESPIRE Collaboration

Coverage

Start date

01/08/2020

End date

31/01/2021

Time lag

Not applicable

Geographic coverage

Pakistan

Maximum age range

150

Accessibility

Language

en

Controlled vocabulary

LOCAL

Format

text

Data Access Request

Dataset pipeline status

Not available

Time to dataset access

Not applicable

Access method category

Varies based on project

Access service description

Access is managed on a project-by-project basis. Contact the RESPIRE team.

Jurisdiction

PK

Data Controller

RESPIRE

Data Processor

RESPIRE

Dataset Types: Health and disease


Collection Sources:

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