| Autor: |
Qi, Yiming, Zhang, Xiaochun, Shen, Zhiyun, Liang, Yixiu, Chen, Shasha, Pan, Wenzhi, Zhou, Daxin, Ge, Junbo |
| Zdroj: |
Journal of Cardiovascular Translational Research; Dec2024, Vol. 17 Issue 6, p1328-1337, 10p |
| Abstrakt: |
This study aimed to develop a force analysis model correlating fluoroscopic images of self-expandable valves with stress distribution. For this purpose, a nonmetallic measuring device designed to apply diverse forces at specific positions on a valve stent while simultaneously measuring force magnitude was manufactured, obtaining 465 sets of fluorescent films under different force conditions, resulting in 5580 images and their corresponding force tables. Using the XrayGLM, a mechanical analysis model based on valve fluorescence images was trained. The accuracy of the image force analysis using this model was approximately 70% (50–88.3%), with a relative accuracy of 93.3% (75–100%). This confirms that fluoroscopic images of transcatheter aortic valve replacement (TAVR) valve stents contain a wealth of mechanical information, and machine learning can be used to train models to recognize the relationship between stent images and force distribution, enhancing the understanding of TAVR complications. Force distribution analysis through valve fluoroscopy image: correlation model between valve fluorescence images and stress conditions was trained using large-scale data to assess force distribution based on valve morphology. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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