Your browser doesn't support javascript.
loading
Force Analysis Using Self-Expandable Valve Fluoroscopic Imaging: a way Through Artificial Intelligence.
Qi, Yiming; Zhang, Xiaochun; Shen, Zhiyun; Liang, Yixiu; Chen, Shasha; Pan, Wenzhi; Zhou, Daxin; Ge, Junbo.
Afiliación
  • Qi Y; Department of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
  • Zhang X; National Clinical Research Center for Interventional Medicine, 180 Fenglin Road, Shanghai, China.
  • Shen Z; Department of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
  • Liang Y; National Clinical Research Center for Interventional Medicine, 180 Fenglin Road, Shanghai, China.
  • Chen S; Department of Nursing, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China.
  • Pan W; Department of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
  • Zhou D; National Clinical Research Center for Interventional Medicine, 180 Fenglin Road, Shanghai, China.
  • Ge J; Department of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
J Cardiovasc Transl Res ; 2024 Aug 01.
Article en En | MEDLINE | ID: mdl-39090482
ABSTRACT
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.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Cardiovasc Transl Res Asunto de la revista: ANGIOLOGIA / CARDIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Cardiovasc Transl Res Asunto de la revista: ANGIOLOGIA / CARDIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos