Current Status of Radiomics and Deep Learning in Liver Imaging. Issue 3 (May 2021)

• Record Type: Journal Article
• Authors: Chu, Linda C.; Park, Seyoun; Kawamoto, Satomi; Yuille, Alan L.; Hruban, Ralph H.; Fishman, Elliot K.
• Is Part Of: Journal of computer assisted tomography. Volume 45:Issue 3(2021)
• Publication Date: 2021-05
• Subjects: artificial intelligence -- deep learning -- radiomics -- convolutional neural network -- liver; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001169
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Abstract: Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.