Clinical applications of artificial intelligence in liver imaging.
Artificial intelligence
Latent Dirichlet allocation
Liver imaging
Topic analysis
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
30
03
2023
accepted:
21
04
2023
medline:
15
6
2023
pubmed:
11
5
2023
entrez:
10
5
2023
Statut:
ppublish
Résumé
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
Identifiants
pubmed: 37165151
doi: 10.1007/s11547-023-01638-1
pii: 10.1007/s11547-023-01638-1
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
655-667Informations de copyright
© 2023. Italian Society of Medical Radiology.
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