This Artificial Intelligence model interprets 12-lead electrocardiograms of adults to diagnose heart failure accuratelyefficiently. It is a fabulous heart failure screening tool that can detect more asymptomatic patients. Our goal is to help patients get early treatmentprevent disease deterioration.

In this technology, a series of potent MOR/NOP agonists has been developed, which demonstrated potent antinociception in tail flick and cancer pain mouse models with fewer side-effects in respiratory suppression, heart rate decreasing, and constipation models than morphine.

The HeaortaNet is developed by the TW-CVAI team. The HeaortaNet is a deep learning model trained by 70,000 axial images from 200 patients with verified annotations of the pericardiumaorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardiumaorta, to 0.4 seconds. The segmentation accuracy, as assessed by dice similarity coefficient, is 94.8 for the pericardium,91.6 for the aorta. The imaging-based Cardiovascular Risk Prediction module was constructed by analyzing data from the National Health Insurance Databank.