[口头报告]Ultrasonic image fibrosis staging based on machine learning for chronic liver disease - 报告详情

Ultrasonic image fibrosis staging based on machine learning for chronic liver disease
编号:96 稿件编号:70 访问权限:仅限参会人 更新:2021-10-30 22:07:56 浏览:182次 口头报告

报告开始:2021年11月14日 21:15 (Asia/Shanghai)

报告时间:25min

所在会议:[PS1] Plenary Session 1 [CT1] Workshop on CT

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摘要
The purpose of this study is to use machine learning based methods to classify the liver fibrosis staging of chronic liver disease(CLD) using ultrasound images. This study has recruited 187 patients from Ditan Hospital. Liver biopsies were used as the gold standard. Two classification approaches are implemented in our work. The EfficientNet that is based on the conventional convolutional neural network (CNN) is used for classification. The second approach is a radiomics model. We investigated 637 radiomics features and the redundant features were reduced by the least absolute shrinkage and selection operator (LASSO). After reduction, fewer than 20 independent features are used for classifications. The area under the receiver operating characteristic (AUC) of EfficientNet model for cirrhosis (F4), advanced fibrosis (F3+F4), and significant fibrosis (F2+F3+F4) were 0.83, 0.78, 0.84, relatively. The AUC values of radiomics model for cirrhosis, advanced fibrosis, and significant fibrosis were 0.96, 0.81, 0.85, relatively. Machine learning methods can obviously classify liver fibrosis by CLD ultrasound image.
关键字
Liver fibrosis, Ultrasound images, Machine learning, Classification
报告人
Yumeng Zhang
研究生 Capital Medical University

稿件作者
Yumeng Zhang Capital Medical University
Yao Zhang Beijing Ditan hospital
Yunxian Zhang Capital Medical University
Dan Wang Capital Medical University
Fan Peng Capital Medical University
Shangqi Cui Capital Medical University
Zhi Yang Capital Medical University
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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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