Unraveling the complexity of hypertrophic cardiomyopathy: a machine learning-based radiomics model to predict phenotype and cardiovascular events

被引:0
|
作者
Miranda, I. Ines [1 ]
Passos, M. [1 ]
Gerardo, F. [1 ]
Sarmento, M. [1 ]
Mateus, C. [1 ]
Lopes, J. [1 ]
Antunes, M. [2 ]
Ferreira, V. [2 ]
Rosa, S. [2 ]
Augusto, J. [1 ]
机构
[1] Hosp Prof Doutor Fernando Fonseca, Amadora, Portugal
[2] Hosp Santa Marta, Lisbon, Portugal
关键词
D O I
暂无
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
引用
收藏
页码:520 / 520
页数:1
相关论文
共 50 条
  • [41] Genotype phenotype relation in patients with hypertrophic cardiomyopathy: development of a model to predict the genetic yield
    Robyns, T.
    Breckpot, J.
    Nuyens, D.
    Vandenberk, B.
    Corveleyn, A.
    Kuiperi, C.
    Van Aelst, L.
    Van Cleemput, J.
    Willems, R.
    EUROPEAN HEART JOURNAL, 2018, 39 : 1322 - 1322
  • [42] A machine learning-based model to predict the 15-year risk for cardiovascular disease in a cohort of people living with HIV
    Muccini, C.
    Masci, C.
    Corso, F.
    Galli, L.
    Poli, A.
    Ranzenigo, M.
    Monardo, R.
    Paganoni, A. M.
    Castagna, A.
    Leva, F.
    HIV MEDICINE, 2021, 22 : 143 - 144
  • [43] Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype
    Segar, Matthew W.
    Usman, Muhammad Shariq
    Patel, Kershaw V.
    Khan, Muhammad Shahzeb
    Butler, Javed
    Manjunath, Lakshman
    Lam, Carolyn S. P.
    Verma, Subodh
    Willett, DuWayne
    Kao, David
    Januzzi, James L.
    Pandey, Ambarish
    EUROPEAN JOURNAL OF HEART FAILURE, 2024, 26 (10) : 2183 - 2192
  • [44] A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease
    Xia, Yuhan
    Huang, Yuezhong
    Gong, Min
    Liu, Weirong
    Meng, Yuanhui
    Wu, Huiyang
    Zhang, Hui
    Zhang, Hao
    Weng, Luyi
    Chen, Xiao-Li
    Qiu, Huixian
    Rong, Xing
    Wu, Rongzhou
    Chu, Maoping
    Huang, Xiu-Feng
    ISCIENCE, 2025, 28 (03)
  • [45] Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases
    Marjaneh Taghavi
    Stefano Trebeschi
    Rita Simões
    David B. Meek
    Rianne C. J. Beckers
    Doenja M. J. Lambregts
    Cornelis Verhoef
    Janneke B. Houwers
    Uulke A. van der Heide
    Regina G. H. Beets-Tan
    Monique Maas
    Abdominal Radiology, 2021, 46 : 249 - 256
  • [46] Machine learning-based radiomics models accurately predict Crohn's disease-related anorectal cancer
    Horio, Yuki
    Ikeda, Jota
    Matsumoto, Kentaro
    Okada, Shinichiro
    Nagano, Kentaro
    Kusunoki, Kurando
    Kuwahara, Ryuichi
    Kimura, Kei
    Kataoka, Kozo
    Beppu, Naohito
    Uchino, Motoi
    Ikeda, Masataka
    Okadome, Takeshi
    Yamakado, Koichiro
    Ikeuchi, Hiroki
    ONCOLOGY LETTERS, 2024, 28 (03)
  • [47] Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases
    Taghavi, Marjaneh
    Trebeschi, Stefano
    Simoes, Rita
    Meek, David B.
    Beckers, Rianne C. J.
    Lambregts, Doenja M. J.
    Verhoef, Cornelis
    Houwers, Janneke B.
    van der Heide, Uulke A.
    Beets-Tan, Regina G. H.
    Maas, Monique
    ABDOMINAL RADIOLOGY, 2021, 46 (01) : 249 - 256
  • [48] Machine learning model using cardiovascular magnetic resonance to predict cardiovascular events in asymptomatic patients with known CAD
    Amar, J.
    Garot, J.
    Toupin, S.
    Unger, A.
    Goncalves, T.
    Duhamel, S.
    Garot, P.
    Unterseeh, T.
    Champagne, S.
    Hovasse, T.
    Dillinger, J. G.
    Henry, P.
    Bousson, V
    Sanguineti, F.
    Pezel, T.
    EUROPEAN HEART JOURNAL, 2024, 45
  • [49] Evaluating Methods to Mitigate the Bias for Machine Learning-Based Cardiovascular Risk Model
    Li, Fuchen
    Zhao, Juan
    Wu, Patrick
    Ong, Henry H.
    Wei, Wei-qi
    Peterson, Josh F.
    CIRCULATION, 2022, 146
  • [50] Machine learning-based modeling to predict inhibitors of acetylcholinesterase
    Hardeep Sandhu
    Rajaram Naresh Kumar
    Prabha Garg
    Molecular Diversity, 2022, 26 : 331 - 340