Coronavirus Clinical Trial
— AI-COV-19Official title:
Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms and Imaging
NCT number | NCT04510441 |
Other study ID # | 20HH5967 |
Secondary ID | |
Status | Recruiting |
Phase | |
First received | |
Last updated | |
Start date | May 26, 2020 |
Est. completion date | May 1, 2022 |
Verified date | August 2021 |
Source | Imperial College London |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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. On 11 March 2020, the WHO made the assessment that COVID-19 can be characterised as a pandemic. With the development of machine learning, deep learning based artificial intelligence (AI) technology has demonstrated tremendous success in the field of medical data analysis due to its capacity of extracting rich features from imaging and complex clinical datasets. In this study, we aim to use clinical data collected as part of routine clinical care (heart tracings, X-rays and CT scans) to train artificial intelligence and machine learning algorithms, to accurately predict the course of disease in patients with Covid-19 infection, using these datasets.
Status | Recruiting |
Enrollment | 2000 |
Est. completion date | May 1, 2022 |
Est. primary completion date | May 1, 2022 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
United Kingdom | Chelsea and Westminster Hospital NHS Foundation Trust | London | |
United Kingdom | Imperial College London (Hammersmith campus) | London | |
United Kingdom | London North West University Healthcare NHS Trust | London | |
United Kingdom | St Mary's Hospital | London |
Lead Sponsor | Collaborator |
---|---|
Imperial College London | Chelsea and Westminster Hospital NHS Foundation Trust, Imperial College Healthcare NHS Trust, London North West Healthcare NHS Trust |
United Kingdom,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Accuracy of machine learning to be able to predict outcome of coronavirus (COVID-19) infection | Accuracy with which computer based analysis (machine learning) can diagnose and/or prognosticate Covid-19 Number of Participants With COVID19 who died or survived following hospital admission | At the end of data analyses, approximately 1 year | |
Primary | Accuracy of machine learning to be able to predict prognosis of coronavirus (COVID-19) infection | Number of participants who required invasive vs non-invasive ventilation vs ward-based care vs died | At the end of data analyses, approximately 1 year | |
Secondary | Accuracy of machine learning to be able to predict cardiac involvement of coronavirus (COVID-19) infection | Number of participants who had COVID19-related heart problems. | At the end of data analyses, approximately 1 year | |
Secondary | Accuracy of machine learning vs human assessment to diagnose coronavirus (COVID-19) infection | Number of participants that can be identified as having COVID19 using machine learning vs human or other clinical test or assessment | At the end of data analyses, approximately 1 year |
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