黑料网吃瓜爆料

From strokes to congenital heart conditions, many serious illnesses are linked to the way blood flows through our arteries. For years, researchers have used computer simulations to study this flow, but the process has often been slow and limited to specialist areas, such as analysing carotid artery disease or testing treatment options for rare heart conditions. 

Now, researchers at 黑料网吃瓜爆料 are taking this a step further. Led by Dr Jie Wang, the team have developed an AI-powered 鈥榙igital twin鈥� of the human vascular system that makes patient-specific blood flow simulations faster, more accurate and more accessible for clinical use. 

By combining advanced computer models with machine learning, their system can quickly predict key indicators such as pressure and wall shear stress 鈥� measures that help doctors understand how blood moves and where risks may lie. Unlike traditional methods that require time-intensive, high-powered computing, this approach runs efficiently on the open-source AortaCFD app, paving the way for real-time, personalised insights. 

As Dr Wang explains, their long-term goal is to give clinicians a powerful tool for precision care: 鈥淥ur work uses advanced computer modelling to create realistic 鈥渄igital twins鈥� of patients鈥� blood flow, allowing doctors to simulate and predict how blood moves through the body quickly and accurately.  

鈥淏y combining different modelling techniques, the research turns complex simulations into practical tools that help clinicians plan personalised treatments.  

鈥淔uture work will use AI methods, such as physics-informed neural networks (PINNs), to make these predictions even faster and more precise.鈥�