SARS-CoV-2 Infection Clinical Trial
Official title:
Evaluation of the Artificial Intelligence/Machine Learning-Based Diagnostic Software as a Medical Device Using Forced Cough Vocalization Signal Data Signatures in the Diagnosis of COVID-19 Illness: A Prospective, Two-Arm Non-Inferiority Clinical Validation Trial of AudibleHealth Dx Software as a Medical Device (EUA-US)
The AudibleHealth Dx is a diagnostic software as a medical device (Dx SaMD) consisting of an ensemble of software subroutines that interacts with a proprietary database of Signal Data Signatures (SDS), using Artificial Intelligence/Machine Learning (AI/ML) to analyze forced cough vocalization signal data signatures (FCV-SDS) for diagnostic purposes. This study will evaluate the performance of the AudibleHealth Dx in comparison to a standard of care Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID-19. A secondary purpose of the study will be usability testing of the device for participants and providers.
The study is a prospective, multi-site, non-inferiority trial comparing the AudibleHealth Dx to FDA approved COVID-19 RT-PCR testing to demonstrate non-inferiority of the PPA and NPA when using this device to diagnose COVID-19 illness. The AudibleHealth Dx test and the "BioFire Respiratory 2.1 (RP2.1)" (brand name) test will be performed for each participant during a single encounter. Participants and staff will be blinded to AudibleHealth Dx results and the RT-PCR status at the time of testing. No one will know both results in real-time except for the Site Coordinators and unblinded statistician specifically authorized to have these results for enrollment, audit, data tracking, and data compiling purposes. • Unblinding of the results will occur after the AudibleHealth Dx, RT-PCR, and the second RT-PCR results (if necessary for discordance) have been obtained. Results for the RT-PCR test will be received by the participant according to the clinical site's protocol. Target enrollment for this trial will be 65 COVID-19 positive cases and 152 COVID-19 negative cases, presuming a prevalence of 0.30 for a total of 217 subjects meeting all inclusion criteria. ;
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