Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. Itis highly contagious, and severe cases can lead to acute respiratory distress or multipleorgan failure. On 11 March 2020, the WHO made the assessment that COVID-19 can becharacterised as a pandemic. With the development of machine learning, deep learningbased artificial intelligence (AI) technology has demonstrated tremendous success in thefield of medical data analysis due to its capacity of extracting rich features fromimaging and complex clinical datasets. In this study, we aim to use clinical datacollected as part of routine clinical care (heart tracings, X-rays and CT scans) to trainartificial intelligence and machine learning algorithms, to accurately predict the courseof disease in patients with Covid-19 infection, using these datasets.
Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. It
is highly contagious, and severe cases can lead to acute respiratory distress or multiple
organ failure and ultimately death. The disease can be confirmed by using the
reverse-transcription polymerase chain reaction (RT-PCR) test. ECGs, Chest x-rays and CT
scans are rich sources of data that provide insight to disease that otherwise would not
be available.
Knowing who to admit to the hospital or intensive care saves lives as it helps to
mitigate resource shortages. Novel Artificial Intelligence tools such as Deep learning
will allow a complex assessment of the Imaging and clinical data that could potentially
help clinicians to make a faster and more accurate diagnosis, better triage patients and
assess treatment response and ultimately better prediction of outcome. Our group has
significant experience implementing machine learning algorithms on vast quantities of
ECGs, such as from the UK Biobank, and propose to extend our techniques to data from
patients with Covid-19.
This is a retrospective data study on patients with suspicious and confirmed COVID-19.
The study aims to recruit up to 2000 patients who will be referred to have ECGs, chest
X-rays or CT scans at Chelsea and Westminster Hospital NHS Foundation Trust, Imperial
College Healthcare NHS Trust and London North West London University Healthcare NHS
Trust.
To be included in this study, the patient must:
- have ECGs, Chest x-ray and/or chest CT imaging (with or without contrast)
- laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat swab samples)
or clinical suspicion for Covid19 infection
- be aged >18 years Patients with suboptimal ECGs, chest radiograph and CT studies due
to artefacts will be excluded. Patients will also be excluded if the time-interval
between ECGs, chest CT and the RT-PCR assay was longer than 7 days.
This study received HRA and Health and Care Research Wales (HCRW) approval on 18 May 2020
following review by Research Ethics Committee at a meeting held on 13 May 2020(Protocol
number: 20HH5967; REC reference: 20/HRA/2467).
Other: Nil intervention
Nil intervention; retrospective cohort study
Inclusion Criteria:
- have ECGs, Chest x-ray and/or chest CT imaging (with or without contrast)
- positive laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat swab
samples) or clinical suspicion for Covid-19 infection
- be aged >18 years
Exclusion Criteria:
- Suboptimal ECGs, chest radiographs or CT studies for deep learning methods due to
artefacts including severe
- motion artefacts which causes blurring of the contours of or significant artefacts
due to metallic prosthesis which causes image degradation
- Time-interval between ECGs, chest CT and the RT-PCR assay was longer than 7 days
London North West University Healthcare NHS Trust
London, United Kingdom
Chelsea and Westminster Hospital NHS Foundation Trust
London, United Kingdom
Imperial College London (Hammersmith campus)
London, United Kingdom
St Mary's Hospital
London, United Kingdom