Ultrasound artificial intelligence in the assessment of liver fibrosis
Liver fibrosis is a necessary step in the progression of various chronic liver diseases to cirrhosis and hepatocellular carcinoma, so early diagnosis of liver fibrosis is of great clinical significance. Ultrasound is the first choice for diagnosing liver fibrosis and has been used as a complementar...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
The Editorial Department of Chinese Journal of Clinical Research
2025-05-01
|
| Series: | Zhongguo linchuang yanjiu |
| Subjects: | |
| Online Access: | http://zglcyj.ijournals.cn/zglcyj/ch/reader/create_pdf.aspx?file_no=20250532 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850254575701852160 |
|---|---|
| author | HUANG Xiulin, CHEN Chengcai, LUO Tingting, LU Jiao, LIU Anxin, FENG Shiwen |
| author_facet | HUANG Xiulin, CHEN Chengcai, LUO Tingting, LU Jiao, LIU Anxin, FENG Shiwen |
| author_sort | HUANG Xiulin, CHEN Chengcai, LUO Tingting, LU Jiao, LIU Anxin, FENG Shiwen |
| collection | DOAJ |
| description | Liver fibrosis is a necessary step in the progression of various chronic liver diseases to cirrhosis and hepatocellular carcinoma, so early diagnosis of liver fibrosis is of great clinical significance. Ultrasound is the first choice for diagnosing liver fibrosis and has been used as a complementary method to liver biopsy, but it is subjective and lacks uniform quantitative standards in diagnosing liver fibrosis. In recent years, artificial intelligence (AI) has been developed and integrated into ultrasound to make up for the artificial errors of traditional ultrasound, especially imaging histology and deep learning, which can extract more features reflecting disease information from a large number of ultrasound images. Among them, the AI technology based on gray.scale ultrasound and ultrasonic elastography has achieved good staging performance and also faced some challenges. Therefore, this paper reviews the application of ultrasound AI technology in staging of liver fibrosis and the objective challenges faced nowadays. |
| format | Article |
| id | doaj-art-adaf7b2631cd4aaab76fc6e09d3a0cb9 |
| institution | OA Journals |
| issn | 1674-8182 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | The Editorial Department of Chinese Journal of Clinical Research |
| record_format | Article |
| series | Zhongguo linchuang yanjiu |
| spelling | doaj-art-adaf7b2631cd4aaab76fc6e09d3a0cb92025-08-20T01:57:05ZzhoThe Editorial Department of Chinese Journal of Clinical ResearchZhongguo linchuang yanjiu1674-81822025-05-0138580680810.13429/j.cnki.cjcr.2025.05.032Ultrasound artificial intelligence in the assessment of liver fibrosisHUANG Xiulin, CHEN Chengcai, LUO Tingting, LU Jiao, LIU Anxin, FENG Shiwen0Graduate School, Youjiang Medical University for Nationalities, Baise, Guangxi 533000, China; Department of Ultrasound Medicine, Affiliated Hospital of Youjiang University of Ethnic Medicine, Baise, Guangxi 533000, China Liver fibrosis is a necessary step in the progression of various chronic liver diseases to cirrhosis and hepatocellular carcinoma, so early diagnosis of liver fibrosis is of great clinical significance. Ultrasound is the first choice for diagnosing liver fibrosis and has been used as a complementary method to liver biopsy, but it is subjective and lacks uniform quantitative standards in diagnosing liver fibrosis. In recent years, artificial intelligence (AI) has been developed and integrated into ultrasound to make up for the artificial errors of traditional ultrasound, especially imaging histology and deep learning, which can extract more features reflecting disease information from a large number of ultrasound images. Among them, the AI technology based on gray.scale ultrasound and ultrasonic elastography has achieved good staging performance and also faced some challenges. Therefore, this paper reviews the application of ultrasound AI technology in staging of liver fibrosis and the objective challenges faced nowadays. http://zglcyj.ijournals.cn/zglcyj/ch/reader/create_pdf.aspx?file_no=20250532liver fibrosisartificial intelligenceradiomicsdeep learningquantitative assessment |
| spellingShingle | HUANG Xiulin, CHEN Chengcai, LUO Tingting, LU Jiao, LIU Anxin, FENG Shiwen Ultrasound artificial intelligence in the assessment of liver fibrosis Zhongguo linchuang yanjiu liver fibrosis artificial intelligence radiomics deep learning quantitative assessment |
| title | Ultrasound artificial intelligence in the assessment of liver fibrosis |
| title_full | Ultrasound artificial intelligence in the assessment of liver fibrosis |
| title_fullStr | Ultrasound artificial intelligence in the assessment of liver fibrosis |
| title_full_unstemmed | Ultrasound artificial intelligence in the assessment of liver fibrosis |
| title_short | Ultrasound artificial intelligence in the assessment of liver fibrosis |
| title_sort | ultrasound artificial intelligence in the assessment of liver fibrosis |
| topic | liver fibrosis artificial intelligence radiomics deep learning quantitative assessment |
| url | http://zglcyj.ijournals.cn/zglcyj/ch/reader/create_pdf.aspx?file_no=20250532 |
| work_keys_str_mv | AT huangxiulinchenchengcailuotingtinglujiaoliuanxinfengshiwen ultrasoundartificialintelligenceintheassessmentofliverfibrosis |