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dc.contributor.authorRadiya, Keyur
dc.contributor.authorJoakimsen, Henrik Lykke
dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorAahlin, Eirik Kjus
dc.contributor.authorLindsetmo, Rolf Ole
dc.contributor.authorMortensen, Kim Erlend
dc.date.accessioned2023-08-14T06:54:45Z
dc.date.available2023-08-14T06:54:45Z
dc.date.issued2023-05-12
dc.description.abstractObjectives Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?<p> <p>Methods A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. <p>Results One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians’ intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identifed. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. <p>Conclusion Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.en_US
dc.identifier.citationRadiya, Joakimsen, Mikalsen, Aahlin, Lindsetmo, Mortensen. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. European Radiology. 2023en_US
dc.identifier.cristinIDFRIDAID 2152061
dc.identifier.doi10.1007/s00330-023-09609-w
dc.identifier.issn0938-7994
dc.identifier.issn1432-1084
dc.identifier.urihttps://hdl.handle.net/10037/29887
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.journalEuropean Radiology
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titlePerformance and clinical applicability of machine learning in liver computed tomography imaging: a systematic reviewen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)