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.

Pacemaking dysfunction has become a significant disease that may contribute to heart rhythm disorders, syncope, and even death. Up to now, the best way to treat it is to implant electronic pacemakers. However, these have many disadvantages such as limited battery life, infection, and fixed pacing rate. There is an urgent need for a biological pacemaker (bio-pacemaker). This is expected to replace electronic devices because of its low risk of complications and the ability to respond to emotion. Here we survey the contemporary development of the bio-pacemaker by both experimental and computational approaches. The former mainly includes gene therapy and cell therapy, whilst the latter involves the use of multi-scale computer models of the heart, ranging from the single cell to the tissue slice. Up to now, a bio-pacemaker has been successfully applied in big mammals, but it still has a long way from clinical uses for the treatment of human heart diseases. It is hoped that the use of the computational model of a bio-pacemaker may accelerate this process. Finally, we propose potential research directions for generating a bio-pacemaker based on cardiac computational modeling.