Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework
Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which...
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Published in | Frontiers in genetics Vol. 14; p. 1004481 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
07.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 Edited by: Yucong Duan, Hainan University, China Lauren Erdman, University of Toronto, Canada This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics These authors have contributed equally to this work Reviewed by: Jonathan Soldera, University of Caxias do Sul, Brazil |
ISSN: | 1664-8021 1664-8021 |
DOI: | 10.3389/fgene.2023.1004481 |