What To Know
- VUNO Med®–DeepCARS™, once implemented in clinical practice, could enable more rapid and efficient responses to in-hospital cardiac arrest by predicting a cardiac event using vital signs automatically collected from a patient’s EMR.
- The ability to utilize key vital signs, which are routinely collected from in-ward patients, can facilitate broader adoption of the device in a wide range of clinical environments.
South Korean artificial intelligence (AI) developer, VUNO Inc. announced today that it has received regulatory approval from the Ministry of Food and Drug Safety (MFDS) for its VUNO Med®–DeepCARS™, an AI medical device for cardiac arrest prediction through vital signs.
VUNO Med®–DeepCARS™ is a breakthrough AI-driven medical device that analyzes the potential risk of cardiac arrest using four primary vital signs:
- Blood Pressure (diastolic and systolic)
- Respiratory Rate
- Heart Rate
- Body Temperature
These data points are collected from the electronic medical record (EMR) of hospitalized patients. It has been shown to provide reliable early cardiac arrest prediction which can enable medical practitioners to mount a rapid and effective response.
VUNO Med®–DeepCARS™ has been studied in a clinical trial at the Asan Medical Center (Seoul, S. Korea) and has articles published in top emergency medicine journals including; Resuscitation, Journal of the American Heart Association (JAHA) and Critical Care Medicine (CCM).
In September 2020, the device was designated as a “Breakthrough Medical Device” by the MFDS in recognition of both its technological merit and its innovative efficacy in the clinical settings.
General hospital wards have been noted for having limited capacity to respond to rapidly-deteriorating patients compared to intensive care units due to the difficulty of constant patient monitoring. In-hospital cardiac arrest, with a high mortality rate of 75%[1], strikes over 290,000 hospital patients each year in the United States alone[2].
VUNO Med®–DeepCARS™, once implemented in clinical practice, could enable more rapid and efficient responses to in-hospital cardiac arrest by predicting a cardiac event using vital signs automatically collected from a patient’s EMR. The ability to utilize key vital signs, which are routinely collected from in-ward patients, can facilitate broader adoption of the device in a wide range of clinical environments.
VUNO Chairman Lee Yeha said, “We seek to help save lives by urging the rapid adoption of VUNO Med®–DeepCARS™ in hospitals,” adding that “the regulatory approval of VUNO Med®–DeepCARS™ heralds the beginning of a wider application of our highly-promising biosignal-based AI technology.”
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[1] Ji Yeoun Kim et al., In-hospital Cardiopulmonary Resuscitation: Incidence and Survival Rate according to the Utstein Template. Korean Journal of Anesthesiology. 2002, vol.43, no.4, pp. 443-450 |
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[2] Andersen LW et al., In-Hospital Cardiac Arrest: A Review. JAMA. 2019;321(12):1200–1210. doi:10.1001/jama.2019.1696 |