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摘要:
Using the latest available artificial intelligence (AI) technology, an advanced algorithm LIVERFAStTM has been used to evaluate the diagnostic accuracy of machine learning (ML) biomarker algorithms to assess liver damage. Prevalence of NAFLD (Nonalcoholic fatty liver disease) and resulting NASH (nonalcoholic steatohepatitis) are constantly increasing worldwide, creating challenges for screening as the diagnosis for NASH requires invasive liver biopsy. Key issues in NAFLD patients are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. In this prospective study, the staging of three different lesions of the liver to diagnose fatty liver was analyzed using a proprietary ML algorithm LIVERFAStTM developed with a database of 2862 unique medical assessments of biomarkers, where 1027 assessments were used to train the algorithm and 1835 constituted the validation set. Data of 13,068 patients who underwent the LIVERFAStTM test for evaluation of fatty liver disease were analysed. Data evaluation revealed 11% of the patients exhibited significant fibrosis with fibrosis scores 0.6 - 1.00. Approximately 7% of the population had severe hepatic inflammation. Steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and Fibrosis) scores obtained using the LIVERFAStTM algorithm, NAFLD was detected in 13.41% of the patients (Sx > 0, Ay 0). Approximately 1.91% (Sx > 0, Ay = 2, Fz > 0) of the patients showed NAFLD or NASH scorings while 1.08% had confirmed NASH (Sx > 0, Ay > 2, Fz = 1 - 2) and 1.49% had advanced NASH (Sx > 0, Ay > 2, Fz = 3 - 4). The modified SAF scoring system generated by LIVERFAStTM provides a simple and convenient evaluation of NAFLD and NASH in a cohort of Southeast Asians. This system may lead to the use of noninvasive liver tests in extended populations for more accurate diagnosis of liver pathology, prediction of clinical path of individuals at all stages of liver diseases, and provis
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篇名 Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFASt<sup>TM</sup>)
来源期刊 智能学习系统与应用(英文) 学科 医学
关键词 Machine Learning (ML) Artificial Intelligence (AI) Neural Networks (NNs) STEATOSIS INFLAMMATION ACTIVITY Fibrosis (SAF Score) NONALCOHOLIC Fatty Liver Disease (NAFLD) Non-Alcoholic STEATOHEPATITIS (NASH)
年,卷(期) 2020,(2) 所属期刊栏目
研究方向 页码范围 31-49
页数 19页 分类号 R57
字数 语种
DOI
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研究主题发展历程
节点文献
Machine
Learning
(ML)
Artificial
Intelligence
(AI)
Neural
Networks
(NNs)
STEATOSIS
INFLAMMATION
ACTIVITY
Fibrosis
(SAF
Score)
NONALCOHOLIC
Fatty
Liver
Disease
(NAFLD)
Non-Alcoholic
STEATOHEPATITIS
(NASH)
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
智能学习系统与应用(英文)
季刊
2150-8402
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
166
总下载数(次)
0
总被引数(次)
0
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