Covid19
Conditions
Brief summary
This study investigates the diagnostic performance of an AI algorithm in the detection of COVID-19 pneumonia on chest radiographs.
Detailed description
This is an international multi-center study. Chest radiographs (CXR) from different participating centers will be collected to develop an AI algorithm to detect COVID-19 pneumonia. This will be tested on external hold out datasets from different centers using SARS-CoV-2 by Real-Time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) Assay as ground truth.
Interventions
DIAGNOSTIC_TESTAI model
Deep Learning CNN model
Sponsors
Ensemble Group Holdings, LLC
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 120 Years
Healthy volunteers
Yes
Inclusion criteria
* All adult patients \>18 years of age * Attended any of the participating institutes between February 1, 2020 until September, 2020 * Underwent both RT-PCR testing and frontal CXR (within 48 hours of PCR testing) for COVID-19 infection * frontal CXR of patients pre-covid pandemic
Exclusion criteria
* Unavailability of patient demographics and clinical data * Inconclusive RT-PCR results * CXR considered to be of non-diagnostic quality by the clinical radiology research team at each site * CXR not in a retrievable or processable format for AI inference
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Performance of AI model | 9 months | Performance (accuracy, sensitivity, specificity, false-positive rate (FPR), false-negative rate (FNR), and Area Under the Curve (AUC)) of the AI model in detection of COVID-19 pneumonia on their baseline CXR using RT-PCR and historical controls as gold standard in a multi-center / multi-national cohort. |
Countries
Hong Kong
Outcome results
None listed