Study of new ultrasound technology combined with cluster analysis on prediction method of liver-heart integration in patients with liver fibrosis
10.3760/cma.j.cn131148-20240117-00043
- VernacularTitle:基于超声新技术联合聚类分析对肝纤维化患者肝心一体化预测方法的研究
- Author:
Wei ZHANG
1
;
Qince LI
;
Kang ZHOU
;
Tianqi LU
;
Jian JIANG
;
Xiuhua YANG
Author Information
1. 哈尔滨医科大学附属第一医院腹部超声科，哈尔滨 150001
- Keywords:
Vector flow imaging;
Combinational elastography;
Liver fibrosis;
Topological data analysis;
Machine learning
- From:
Chinese Journal of Ultrasonography
2024;33(6):482-488
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the early assessment of hepatocardiac integration based on ultrasonic elasticity and blood flow vector imaging (VFM) technology, in conjunction with unsupervised cluster analysis and supervised machine learning methods.Methods:An observational research design without any intervention was adopted from December 2021 to September 2022, 45 patients with liver cirrhosis, 43 patients with liver fibrosis, and 42 healthy volunteers were selected from the First Affiliated Hospital of Harbin Medical University. Liver combined elasticity technology and VFM technology were used to obtain information on the liver and heart of the subjects, respectively. The acquired data were standardized, and then clustered using topological data analysis (TDA) technology on the processed data. Subsequently, the clustering results were evaluated based on statistical analysis, and finally, supervised multi-classification tasks were realized through machine learning methods.Results:Patients were stratified into five distinct groups based on a network of patient similarities. The average characteristics of each group were as follows: Group 1 exhibited the most severe hepatocardiac conditions relative to the other groups. Groups 2 and 3 displayed moderately severe conditions.In contrast, Group 4 comprised entirely of healthy controls, all of whom presented with normal hepatocardiac function. Group 5 presented a unique case among the categories.Participants in this group showed poor liver conditions. However, according to the guidelines for cardiac diastolic function assessment, their heart function was generally unremarkable, with only a minority of indicators deviating significantly. Support Vector Machine (SVM), Random Forest Tree (RFT), and Multilayer Perceptron (MLP) were employed for multi-classification tasks on the test dataset. The average accuracies achieved by these models were 70%, 81%, and 84%, respectively.Conclusions:By combining liver combined ultrasonic elasticity, cardiac VFM technology and TDA technology to construct a patient similarity network, we successfully identified patients with liver fibrosis who did not show abnormalities in conventional cardiac indicators but may have potential abnormal cardiac function, which has important implications for guiding the selection of clinical intervention measures, and optimizing patient management stratification.
