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.

In order to develop an AI model that can accuratelycompletely segment coronary arteries, our team, the TW-CVAI, has established a training dataset composed of strictly verified annotations of coronary lumen boundaries in coronary CT angiography (CCTA). We designed a deep learning model, two-channel 3D-UNet, with a priori prerequisite (vesselness prior) to facilitate identification of vascular structures. The final model, the TaiCAD-Net, greatly shortens the CCTA interpretation time from 6 hours to 10 minutes, with the overall segmentation accuracy of 86 by Dice similarity coefficient.

DBPR807, a safehighly specific CXCR4-targeting antagonist, has completed the concept validation of treating acute myocardial infarction (AMI) through both ratmini-pig animal disease models induced by occluding the left anterior descending artery (LAD). When DBPR807 (5 mg/kg, SC) was given in the rat model, a significant heart-protective effect was observed with reducing cardiac infarctionfibrosis by 4341, respectively. In the mini-pig model, DBPR807 (3 mg/kg, IV) treatment was allowed to improve 3-month LVEF up to 52 falling in a standard range between 50~70.