Unsupervised machine learning identifies predictive progression markers of IPF

OBJECTIVES: To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome. METHODS: We studied radiological disease progression in...

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Detalles Bibliográficos
Autores principales: Pan, Jeanny, Hofmanninger, Johannes, Nenning, Karl-Heinz, Prayer, Florian, Röhrich, Sebastian, Sverzellati, Nicola, Poletti, Venerino, Tomassetti, Sara, Weber, Michael, Prosch, Helmut, Langs, Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889455/
https://www.ncbi.nlm.nih.gov/pubmed/36066734
http://dx.doi.org/10.1007/s00330-022-09101-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889455/
https://www.ncbi.nlm.nih.gov/pubmed/36066734
http://dx.doi.org/10.1007/s00330-022-09101-x

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