Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives.
| Autor: | Sabottke CF; Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell, P.O. Box 245067, Tucson, AZ 85724-5067, USA. Electronic address: csabottke@email.arizona.edu., Spieler BM; Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA 70112, USA., Moawad AW; Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA., Elsayes KM; Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA. |
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| Jazyk: | angličtina |
| Zdroj: | Magnetic resonance imaging clinics of North America [Magn Reson Imaging Clin N Am] 2021 Aug; Vol. 29 (3), pp. 451-463. |
| DOI: | 10.1016/j.mric.2021.05.011 |
| Abstrakt: | Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation. (Copyright © 2021 Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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