We are developing AI-driven
medical solutions to help
healthcare
professionals save time, reduce costs,
and improve patient outcomes.
VUNO Med®-DeepCARS® is an early warning system used to
monitor and assess the risk of cardiac arrest through
4 vital signs along with age and the recorded time of
vital signs among inpatients in general ward.
VUNO Med-DeepBrain assists the diagnosis of
neurodegenerative diseases by quantifying brain
atrophy of 104 areas and small vessel disease burden.
VUNO Med-Chest X-ray Triage accurately detects suspected
chest abnormalities indicative of major pulmonary diseases
from chest X-ray images.
Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards
A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards
External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods
Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System
Automated Idiopathic Normal-Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis
Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke
Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg
Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer’s disease using a high-performance interpretable deep learning network
The diagnostic performance and clinical value of deep learning-based nodule detection system concerning infuence of location of pulmonary nodule
Utilizing Synthetic Nodules for Improving Nodule detection in chest radiographs
Utility of a Deep Learning Algorithm for Detection of Reticular Opacity on Chest Radiography in Patients With Interstitial Lung Disease
Added value of deep learning based detection system for multiple major findings on chest radiograph
