Patient Risk Stratification Algorithm

AI-based computational tool for assessing patient risk of adverse cardiac events. Integrates multiple clinical, imaging, and laboratory parameters to generate personalized risk scores. Supports evidence-based decision-making for patient management and treatment planning.

Proprietary algorithmic system that analyzes heart rate characteristics to calculate patient infection risk. Processes cardiac variability data and generates hourly risk stratification scores to support clinical decision-making in neonatal and pediatric care settings.

Artificial intelligence system for automated classification of genetic variants as pathogenic or benign. Reduces interpretation time and improves diagnostic accuracy.

A software algorithm that automatically identifies and classifies cardiac arrhythmias from electrocardiogram waveforms. The algorithm alerts clinicians to potentially dangerous rhythm disturbances.

An artificial intelligence algorithm analyzing electrocardiogram waveforms for ST-segment and T-wave changes indicative of myocardial ischemia. The algorithm assists in rapid identification of acute coronary syndrome.

Software-based cardiac rhythm analysis system for automated arrhythmia identification and classification. Enhances diagnostic accuracy in cardiac assessment.

Machine learning software for automated detection and classification of cardiac arrhythmias from ECG data. Analyzes heart rhythm patterns to identify pathological conditions and assist in clinical diagnosis. Compatible with multiple cardiac monitoring devices.

AI-based diagnostic tool for early detection and identification of dementia-related complications in brain imaging. The system enables rapid analysis of large patient populations where early intervention is critical before significant neuronal loss occurs.

Machine learning algorithm designed to identify clinically relevant areas on blood smear slides and optimize sample review parameters. Ensures consistent and efficient analysis of peripheral blood samples.

ANRISK® is an AI-powered risk assessment solution that predicts the likelihood of brain aneurysm development using routine health check-up data alone. Designed as a pre-screening tool prior to brain vascular imaging such as MRA or CTA, ANRISK® enables safe, fast, and cost-effective identification of high-risk individuals without additional medical procedures. Powered by an AI algorithm trained on approximately 8.8 million person-years of large-scale healthcare data, ANRISK® classifies individuals into five risk categories and generates personalized analytical reports. The results include a standardized risk score, estimated prevalence and incidence, contributing risk factors, and tailored lifestyle guidance. ANRISK® is optimized for use in hospitals, health screening centers, and corporate health programs, supporting a preventive approach to cerebrovascular disease management.

Machine learning algorithm trained on 400,000+ patient biomechanical datasets for automated analysis of movement patterns and personalized treatment recommendations. Provides predictive risk assessment for movement-related injuries.

Platform for early identification of high-risk patients and complications including sleep disorders and stress-related conditions. Enables prevention of rehospitalizations through timely interventions.