Evaluation of a COVID-19 Pneumonia CXR AI Detection Algorithm
- Conditions
- Covid19
- Registration Number
- NCT04561024
- Lead Sponsor
- Ensemble Group Holdings, LLC
- 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.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 4000
- 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
- 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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method 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.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
University of Hong Kong
🇭🇰Hong Kong, Hong Kong
University of Hong Kong🇭🇰Hong Kong, Hong Kong