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The algorithm applies AI to wearable sensor data to produce vital signs.
The U.S. Food and Drug Administration (FDA) is maintaining its promise to focus on emerging technologies, as the agency today granted 510(k) clearance for physIQ’s algorithm that continuously determines the respiration rate of ambulatory patients.
PhysIQ applies artificial intelligence (AI) to wearable sensor data to improve outcomes. Its platform collects raw telemetry from a wearable device and uploads it to the cloud. Once in the cloud, FDA-cleared analytics use the raw biosignals to produce vital signs.
“In a real-world environment, respiration rate is a tough vital sign to accurately and consistently measure given high levels of motion artifact,” said Matt Pipke, co-founder and chief transformation officer of physIQ. “Given these challenges, it is ideal to be able to capitalize on the vast processing power and memory in the cloud to iron out the edge cases and outliers.”
The approach enables physIQ to provide vital sign analytics from the cloud, which the company claims fuels higher-level analytics that further characterize humans.
Sensors and wearable technology could transform how patients and providers understand and manage health. But sometimes, it is difficult to analyze the data being collected.
“Accurate and precise vital signs are an essential component of a clinical grade remote intelligence solution but, ultimately, these vital signs are an input into the higher-level AI-based analytics for which physIQ is known,” said Gary Conkright, chairman and CEO of physIQ.
Respiration rate is also a key input to physIQ’s Multivariate Change Index. The index was cleared by the FDA in 2015 and detects changes in the tandem behavior of vital signs based on a personalized baseline of a patient.
The change of vital signs could be evidence of evolving disease exacerbation as a precursor to hospitalization and could provide evidence of drug effectiveness.
“We are encouraged by the successful clearance of respiration as a core dimension of human cardiopulmonary physiology, which will accelerate our development of further AI analytics,” Conkright said.
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