Professor Hao-Min Cheng’s team at National Yang Ming Chiao Tung University has successfully developed an innovative vascular hemodynamics monitoring system. This system not only simplifies the complex analyses previously required but also enables simultaneous acquisition of carotid blood pressure waveforms and flow parameters, further calculating key vascular health indicators such as the Pulse Energy Index.

The Importance of Cardiovascular Health
The pulsatile energy of the carotid artery is a critical indicator of cardiovascular health, reflecting the pressure and impact force generated as blood flows through the vessels. Excessive pulsatile energy may accelerate vascular aging and increase the risk of cardiovascular diseases. Traditional methods for measuring carotid blood pressure and flow parameters rely on complex computational software, limiting their applicability. Moreover, existing techniques that use local ultrasound to measure pulse wave velocity fail to comprehensively reflect the overall impact of pulsatile pressure and flow on organs.

Integration of High-Speed Ultrasound and Deep Learning
This system combines high-speed ultrasound imaging with an optical flow deep learning model, achieving kilohertz-level frame rates for simultaneous measurements of carotid blood pressure and flow. It offers several advantages:

• By synchronously capturing blood pressure and flow data at the same location, the system eliminates discrepancies caused by using different devices, significantly enhancing diagnostic accuracy.
• The Pulse Energy Index quantitatively evaluates vascular aging and predicts risks associated with cognitive impairments.
• Clinical studies have shown that older individuals exhibit significantly higher mean and pulsatile energy levels, further validating the clinical utility of this technology.

Innovative Features of the Technology

• High-Precision Ultrasound Measurements: By overcoming physical limitations of traditional devices and integrating optical flow algorithms, the system accurately analyzes dynamic changes in blood pressure and flow.
• Real-Time Data Processing: The deep learning model processes large volumes of data in real time, providing fast and reliable support for clinical diagnostics.
• Portable Design: The system can be integrated into daily health management devices, increasing accessibility and promoting early screening and disease prevention.

A New Chapter in Personalized Health Management
Professor Cheng stated that this system could be integrated into portable wireless ultrasound devices in the future, enabling continuous monitoring of dynamic hemodynamics and offering groundbreaking solutions for personal health management. Additionally, plans are in place for patent licensing and market collaboration to achieve commercialization within three years, with further development into wearable microdevices aimed at the telemedicine market.

As clinical applications and market demand expand, this system is poised to have a lasting impact in vascular health monitoring and chronic disease management, setting a new benchmark for cardiovascular diagnostics and monitoring technologies.

Resource: 高速超音波成像結合光流深度學習模型