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.

We use features such as asymmetric expression and crooked eyes to assess the risk of facial stroke. Observing the micro vibration of the head caused by the contraction of the heart, and develop a zero-contact facial heart rate and respiration rate detection technology in conjunction with the camera. The technology can accurately measure heart rate and respiration rate in real time, thereby reducing the risk of infection. This technology has obtained two ROC patents (M590433, I689285), two US patents (HEART RATE DETECTION METHOD AND DEVICE THEREOF,MOUTH AND NOSE OCCLUDED DETECTING METHOD AND SYSTEM THEREOF). The possibility of detecting strokes through AI machine learning methods is not only accurate, but also find out signs of stroke early to grasp the best time to seek medical treatment.

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.

Innovative wearable monitoring device for pregnant women can record the signals of contractions, fetal movement,fetal heart rate in real time. With AI intelligent classificationsequential analysis technology, it can link the difficult-to-classify GTC signal patterns with the possibility of fetal distress Finally, the position, sizeduration of fetal movement are further estimated to provide accurate follow-up clinical analysis. It allows pregnant women to monitor the relevant physiological parameters at any time at home to avoid the troublesworries caused by the risk of nosocomial infection of pregnant women.

A flexible PPG sensor patch for measuring pulsations of blood vessels estimating ambulatory blood pressure monitoring (ABPM). The measured quality PPG signals are analyzed to estimate blood pressure (BP), heart rate (HR), blood oxygen (SpO2)atrial fibrillation (A-Fib)avoid motion artifacts. In this way, with subject’s physiological condition apprehended accurately.

This successful technology is the digital interventions on depression patients based on (1) heart rate variability bio-feedbacks (HRVBs) measured by a novel flexible sensor patch attached on patient’s wrist(2) an APP. An innovative flexible low-power PPG patch has been designed which is capable of providing weeks of high-quality PPG signals which are essential towards accurate estimation of HRV for favorable efficacy of designed digital interventions. Also, An AI model correlating the HRVsand PHQ-9/HDRS scores of depression patients will be built based on collected data.