Publications by authors named "Yaodong Hao"
Article Synopsis
- Recent advances in computational fluid dynamics allow for the non-invasive calculation of fractional flow reserve (FFR), but the process is slow; this study develops a machine learning model to improve speed and accuracy in assessing stenosis significance.
- A reduced-order lumped parameter model of the coronary and cardiovascular systems was created, integrated with a machine learning algorithm for predicting flow resistance based on anatomical features, and personalized for individual patients.
- In a study involving 91 patients with 93 lesions, the machine learning-based FFR method demonstrated high diagnostic accuracy (91.4%) and a strong correlation (r=0.86) with traditional invasive FFR, making it a promising tool for efficiently evaluating heart disease.
A simplified coronary model for diagnosis of ischemia-causing coronary stenosis.
Comput Methods Programs Biomed
December 2023
Background And Objective: The functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis.
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