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Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms

NCT04510441 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2021-08-30

No results posted yet for this study

Summary

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.

Conditions

  • Coronavirus

Interventions

OTHER

Nil intervention

Nil intervention; retrospective cohort study

Sponsors & Collaborators

  • Imperial College Healthcare NHS Trust

    collaborator OTHER

  • Chelsea and Westminster NHS Foundation Trust

    collaborator OTHER

  • London North West Healthcare NHS Trust

    collaborator OTHER

  • Imperial College London

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-05-26
Primary Completion
2022-05-01
Completion
2022-05-01

Countries

  • United Kingdom

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04510441 on ClinicalTrials.gov