SensiML™ Corporation, a leading developer of AI tools for building intelligent IoT endpoints, announced that it is collaborating on an effort to use its AI technology to help predict whether people are showing symptoms of COVID-19 infection. One such capability involves using crowdsourcing to collect cough sounds from a large number of volunteers and then analyzing that data combined with other datasets from the consortium using the SensiML Analytics Toolkit to identify the unique cough patterns associated with COVID-19 infections. The goal of the initiative is to give businesses, governments, healthcare, and other public facilities access to multi-sensor pre-diagnostic screening mechanisms to help slow the spread of the disease.
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The initiative is supported by a consortium of companies, universities, and health organizations including Asymmetric Return Capital, SkyWater Technology, and Upward Health, an in-home and virtual care medical provider. In addition to its work with the consortium to build an enhanced screening application, SensiML plans to open-source its own crowdsourced cough sound dataset for researchers at large to access.
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The concept of utilizing AI for pre-diagnostic screening of cough acoustic samples has been studied and validated in recently published academic research and is supported by ongoing projects at multiple esteemed universities. Early published results suggest that using AI to identify coughs as a COVID-19 screening mechanism has significant potential, as the pathomorphology of the disease is distinctive from that of other respiratory diseases.
“There is tremendous need for better pre-diagnostic screening tools as return-to-work measures are put into place across the US and worldwide. The entire SensiML team is excited by the prospect that our edge AI technology can potentially make a positive contribution to the global efforts to combat this pandemic,” said Chris Rogers, SensiML CEO.
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