Evaluation of a COVID-19 Pneumonia CXR AI Detection Algorithm

Ensemble Group Holdings, LLC4,000 enrolled

Overview

This study investigates the diagnostic performance of an AI algorithm in the detection of COVID-19 pneumonia on chest radiographs.

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.

Study Type

OBSERVATIONAL

Enrollment

4,000

Conditions

Covid19

Interventions

AI modelDIAGNOSTIC_TEST

Deep Learning CNN model

Eligibility

Sex: ALLMin age: 18 YearsMax age: 120 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (1)

University of Hong Kong

Hong Kong, Hong Kong

Outcomes

Primary Outcomes

Diagnostic Performance of AI model

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.

Time frame: 9 months

Data from ClinicalTrials.gov

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