Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction.
AI, artificial intelligence
Artificial intelligence
CNN, convolutional neural network
DICOM, Digital Imaging and Communications in Medicine
HCC, hepatocellular carcinoma
ML, machine learning
MVI, microvascular invasion
NAFLD, non-alcoholic fatty liver disease
NASH, non-alcoholic steatohepatitis
TACE, transarterial chemoembolisation
TRIPOD, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis
WSIs, whole slide images
deep learning
diagnostic support system
imaging
machine learning
multimodal data integration
Journal
JHEP reports : innovation in hepatology
ISSN: 2589-5559
Titre abrégé: JHEP Rep
Pays: Netherlands
ID NLM: 101761237
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
13
11
2021
revised:
26
12
2021
accepted:
11
01
2022
entrez:
4
3
2022
pubmed:
5
3
2022
medline:
5
3
2022
Statut:
epublish
Résumé
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
Identifiants
pubmed: 35243281
doi: 10.1016/j.jhepr.2022.100443
pii: S2589-5559(22)00015-5
pmc: PMC8867112
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
100443Informations de copyright
© 2022 The Authors.
Déclaration de conflit d'intérêts
JNK declares consulting services for Owkin (France) and Panakeia (UK) and has received honoraria for scientific talks and participation in advisory boards by MSD, Eisai and Bayer. JC is a consultant for Guerbet, Bayer and Philips. VP is involved in a collaborative study with Owkin, France. DN and TPS declare no conflicts of interest. Please refer to the accompanying ICMJE disclosure forms for further details.
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