Vuno Cardiac Arrest Prediction Solution Published in Prestigious Global Journal... "Approval Expected This Year"

[Asia Economy Reporter Junhyung Lee] VUNO, a domestic medical artificial intelligence (AI) startup, announced on the 19th that a clinical research paper proving the performance of its cardiac arrest prediction software (SW) 'VUNO Med-DeepCARS' was published in the SCI-level academic journal 'Resuscitation.' This journal, published by the European Resuscitation Council since 1972, holds global authority in the field of emergency medicine.

VUNO Med-DeepCARS is an AI solution that predicts the risk of cardiac arrest within the next 24 hours for patients. It analyzes the likelihood of cardiac arrest using blood pressure, pulse, respiration, body temperature, and other data collected from electronic medical records (EMR) of patients admitted to general wards, helping medical staff take preemptive measures.

The study published in the journal was conducted to verify whether VUNO Med-DeepCARS can consistently and effectively predict cardiac arrest in hospitalized patients across various medical settings. VUNO carried out the research at five medium-to-large medical institutions of different sizes and locations, including Seoul National University Bundang Hospital, Samsung Seoul Hospital, and Inha University Hospital. Data from 173,368 adult patients hospitalized over 12 months formed the basis of the study.

Some domestic medical institutions use evaluation indicators such as MEWS (Modified Early Warning Score) to predict and respond to risk situations like cardiac arrest. However, according to VUNO, existing indicators had low sensitivity and a high false alarm rate. In contrast, the study results showed that VUNO Med-DeepCARS had a cardiac arrest prediction accuracy 15.3% higher than the existing MEWS indicator.

The performance in predicting cardiac arrest in advance was also superior to existing indicators. According to the study, VUNO Med-DeepCARS detected more than twice as many cardiac arrest patients 20 hours before the event compared to MEWS.

Lee Yeha, Chairman of VUNO's Board of Directors, said, "This study has proven that VUNO Med-DeepCARS is an effective solution for predicting cardiac arrest in various medical environments," adding, "We will continue to introduce AI solutions based on biosignals, including VUNO Med-DeepCARS, which is expected to obtain regulatory approval this year."