您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Deep Learning-Based Predictive Model for Revascularization of Chronic Total Occlusions on Angiographic Imaging

文献类型： 会议论文

第一作者： Sara Pérez-Martínez

作者： Sara Pérez-Martínez 1 ; Agustín Fernández-Cisnal 2 ; Manuel Pérez-Pelegrí 1 ; Sergio García-Blas 2 ; Gema Mi?ana 2 ; Ernesto Valero 2 ; Juan Sanchis 2 ; David Moratal 1 ;

作者机构： 1.Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, València, Spain

2.Cardiology Department, Hospital Clínico Universitario de València, Instituto de Investigación Sanitaria (INCLIVA), University of València, Centro de Investigación Biomédica en Red Enfermedades Cardiovaculares (CIBERCV), València, Spain

关键词： Deep learning;Biological system modeling;Angiography;Predictive models;Data models;Time measurement;Medical diagnostic imaging

会议名称： Annual International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 1-4

摘要： Revascularization of chronic total occlusions (CTO) is currently one of the most complex procedures in percutaneous coronary intervention (PCI), requiring the use of specific devices and a high level of experience to obtain good results. Once the clinical indication for extensive ischemia or angina uncontrolled with medical treatment has been established, the decision to perform coronary intervention is not simple, since this procedure has a higher rate of complications than non-PCI percutaneous intervention, higher ionizing radiation doses and a lower success rate. However, CTO revascularization has been shown to be helpful in symptomatic improvement of angina, reduction of ischemic burden, or improvement of ejection fraction. The aim of this work is to determine whether a model developed using deep learning techniques, and trained with angiography images, can better predict the likelihood of a successful revascularization procedure for a patient with a chronic total occlusion (CTO) lesion in their coronary artery (measured as procedure success and the duration of time during which X-ray imaging technology is used to perform a medical procedure) than the scales traditionally used. As a preliminary approach, patients with right coronary artery CTO will be included since they present standard angiographic projections that are performed in all patients and present less technical variability (duration, projection angle, image similarity) among them.The ultimate objective is to develop a predictive model to help the clinician in the decision to intervene and to analyze the performance in terms of predicting the success of the technique for the revascularization of chronic occlusions.Clinical Relevance— The development of a deep learning model based on the angiography images could potentially overcome the gold standard and help interventional cardiologists in the treatment decision for percutaneous coronary intervention, maximizing the success rate of coronary intervention.

分类号： q81

  • 相关文献

[1]Adaptive responses of cancer cells to tumor microenvironment changes: an evolutionary model perspective. Chiara Romano,Alessandro Borri,Maria Domenica Di Benedetto. 2024

[2]A High-Resolution Grassland Aboveground Biomass Estimation Method Based on SfM and Grassland Height Factor. Mengdi Cao,Shanshan Cao,Wei Sun,Fantao Kong. 2023

[3]Design and Implementation of Multi-factor Aided Spatial Interpolation Software Tool for Forest Biomass. Wei Sun,Shanshan Cao,Fantao Kong,Jifang Liu. 2023

[4]A new force profile signal for a convex solution of muscle force estimation from electromyographic signals*. Mehdi Shirzadi,Arezoo Mirshamsi,Alp E?refo?lu,Monica Rojas-Martínez,Hamid Reza Marateb,Miquel Angel Ma?anas,Taian Vieira. 2023

[5]Exploring the Impact of Acquisition and Reconstruction Parameters on an Imaging-Based Lung Cancer Risk Model. Luoting Zhuang,Anil Yadav,Grace H Kim,Seyed Mohammad Hossein Tabatabaei,Ashley Prosper,William Hsu. 2024

[6]Pairing Predictive Simulations and Human Experiments in Ankle Exoskeleton Walking. Alexander Weiss,Anthony Chen,Daniel B. Janischowky,Isabella Shih,Gillian C. Phillips,Jessica C. Selinger,Anne D. Koelewijn. 2024

[7]Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns. Daniel Romero Pérez,Jordi Solà Soler,Leon Balchin,Arantxa Mas Serra,Manuel Lujan Torné,Melinda R. Popoviciu Koborzan,Beatriz F. Giraldo. 2023

[8]Division of dataset into training and validation subsets by the jackknife validations to predict the pH optimum for beta-cellobiosidase. Shaomin Yan,Guang Wu. 2022

[9]On The Local Geometry of the Pet Reconstruction Problem. Ramy Merabet,Florent Sureau,Alain Trouvé. 2024

[10]Learning Functional Embedding of Genes Governed by Pair-wised Labels. Cao, Jingjun,Wu, Zhengli,Ye, Wenting,Wang, Haohan. 2017

[11]Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in Ct Angiography. Andrew M. Nguyen,Tejas Sudharshan Mathai,Liangchen Liu,Jianfei Liu,Ronald M. Summers. 2024

[12]Regional Level Investigation of EU-Funded H2020 Collaborations via Social Network Analysis. Ferenc Tolner,Balázs Barta,Gy?rgy Eigner. 2023

[13]Human Body Motion Tracking for Rehabilitation. Moraru Andrei,Eva-H. Dulf,Lehel Dénes-Fazakas,Levente Kovacs. 2024

[14]Measuring High-Resolution Sleep Position in Adolescents over 4 Nights with Smartphone Accelerometers. Yolanda Castillo-Escario,Dolores Blanco-Almazán,Ignasi Ferrer-Lluis,Raimon Jané. 2023

[15]Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning. Potgieter A. B,Camino C,Poblete T,Zhi X,Reynolds-Massey-Reed S,Zhao Y,Belwalkar A,Ruizhu J,George-Jaeggli B,Chapman S,Jordan D,Wu A,Hammer G. L,Zarco-Tejada P. J. 2023

[16]Linear Mixed Effects Modelling of Oxygen Desaturation after Sleep Apneas and Hypopneas: A Pilot Study. Jordi Solà-Soler,Beatriz F. Giraldo,Raimon Jané. 2019

[17]Study of Cell Encapsulation System in Using Thai Silk Fibroin-Based Hydrogel. Nitchaya Suksomthong,Sasitorn Aueviriyavit,Juthamas Ratanavaraporn. 2023

[18]XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images. Yanfeng Zhou,Jiaxing Huang,Chenlong Wang,Le Song,Ge Yang. 2023

[19]Ventricular Conduction System Modeling for Electrophysiological Simulation of the Porcine Heart. Ricardo M Rosales,Konstantinos A Mountris,Manuel Doblaré,Manuel M Mazo,Esther Pueyo. 2022

[20]Automatic Tool for Pulmonary Artery Hemodynamic Assessment from 4D flow MRI. Maialen Stephens,ángel Gaitán,Rahul Kumar,Jorge Nuche,Irene Marco,Juan Delgado,Jesús Ruíz-Cabello,Karen López-Linares,Javier Bóbeda,Haizea Erostarbe. 2023

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台