Cargando…
Radiomics based on artificial intelligence in liver diseases: where are we?
Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treat...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136719/ https://www.ncbi.nlm.nih.gov/pubmed/32280468 http://dx.doi.org/10.1093/gastro/goaa011 |
_version_ | 1783518305514946560 |
---|---|
author | Hu, Wenmo Yang, Huayu Xu, Haifeng Mao, Yilei |
author_facet | Hu, Wenmo Yang, Huayu Xu, Haifeng Mao, Yilei |
author_sort | Hu, Wenmo |
collection | PubMed |
description | Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research. |
format | Online Article Text |
id | pubmed-7136719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71367192020-04-10 Radiomics based on artificial intelligence in liver diseases: where are we? Hu, Wenmo Yang, Huayu Xu, Haifeng Mao, Yilei Gastroenterol Rep (Oxf) Review Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research. Oxford University Press 2020-04-07 /pmc/articles/PMC7136719/ /pubmed/32280468 http://dx.doi.org/10.1093/gastro/goaa011 Text en © The Author(s) 2020. Published by Oxford University Press and Sixth Affiliated Hospital of Sun Yat-sen University http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Hu, Wenmo Yang, Huayu Xu, Haifeng Mao, Yilei Radiomics based on artificial intelligence in liver diseases: where are we? |
title | Radiomics based on artificial intelligence in liver diseases: where are we? |
title_full | Radiomics based on artificial intelligence in liver diseases: where are we? |
title_fullStr | Radiomics based on artificial intelligence in liver diseases: where are we? |
title_full_unstemmed | Radiomics based on artificial intelligence in liver diseases: where are we? |
title_short | Radiomics based on artificial intelligence in liver diseases: where are we? |
title_sort | radiomics based on artificial intelligence in liver diseases: where are we? |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136719/ https://www.ncbi.nlm.nih.gov/pubmed/32280468 http://dx.doi.org/10.1093/gastro/goaa011 |
work_keys_str_mv | AT huwenmo radiomicsbasedonartificialintelligenceinliverdiseaseswherearewe AT yanghuayu radiomicsbasedonartificialintelligenceinliverdiseaseswherearewe AT xuhaifeng radiomicsbasedonartificialintelligenceinliverdiseaseswherearewe AT maoyilei radiomicsbasedonartificialintelligenceinliverdiseaseswherearewe |