Wearable sensors coupled with AI can generate blood pressure readings nearly as accurately as the invasive and risky arterial lines relied on in intensive care units and operating rooms, new work by Johns Hopkins University researchers demonstrates.
Successful initial patient tests suggest the system could become an alternative to arterial catheters and offer hospitals the ability to monitor arterial blood pressure beyond intensive care units.
The sensors could also give people with hypertension a way to continuously monitor their health, like wearable glucose monitors for people with diabetes.
The team created a system called MOSAIC based on two sensors—one placed on a patient's chest and the other on a finger.
Together, the sensors record the heart's electrical activity and the flow of blood through the body and relay these signals to a deep learning model that generates a waveform, a continuous readout of blood pressure over time.
The team is now validating the sensors and algorithm in a larger cohort of Johns Hopkins ICU patients.
Longer term, the researchers hope the system could reduce the need for invasive monitoring and become a way to continuously measure blood pressure in a range of settings—in regular hospital wards and at home.
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