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This study generates robust, uniform clinical data across emerging COVID-19 strains to train ML/AI algorithms of the Sponsor's imPulse™ Una infrasound-to-ultrasound e-stethoscope for digital diagnostic feature synthesis of asymptomatic and symptomatic COVID-19 digital biosignatures for rapid and accurate adult and child mass screening.
Full description
For the next few years or more, the planet probably won't have enough vaccine for everyone. Even as countries with large COVID-19 vaccination programs start pushing to resume travel and trade:
We will not know who is vaccinated and who is not . We will not know who is an (a)symptomatic COVID-19 carrier and who is not.
Because of this, the global community will remain in various stages of masking, social distancing, lock-down, and limited congregation because of cyclical COVID-19 spikes and people will continue to feel unsafe and afraid as novel COVID-19 variants appear and disappear.
This large-scale, multi-site, multi-national study is informed by a completed pilot study at Johns Hopkins- NCT04556149. This study is designed to validate the ability of the imPulse™ Una infrasound-to-ultrasound e-stethoscope to rapidly and accurately screen outpatients with and without confirmed COVID-19 with sensitivity, specificity, positive and negative predictive value matching (PPA >95%) for early, accurate, and rapid, self-directed and point-of-care diagnosis of COVID-19 in areas still lagging in access to vaccines.
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702 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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