| dc.description.abstract | In recent years, chronic liver disease has emerged as a condition significantly impacting health. Effective treatment and proper dietary management in the early stages of liver fibrosis may contribute to recovery. However, non-invasive diagnostic methods such as blood tests and abdominal ultrasound are not highly sensitive for detecting early stages of liver fibrosis. Therefore, in this study, we propose using machine learning (ML) techniques, employing feature extraction, to predict the stages of liver fibrosis. The study comprises experiments on mice and clinical data from human subjects.
In the mouse experiment section, mice were categorized into healthy (A0), mild (A1), moderate (A2), or severe (A3) stages. Specifically, we utilized support vector machines as classifiers, using two types of features: Euler numbers (EN) from mouse magnetic resonance (MR) imaging and estimated porosity related to liver fibrosis. The basic idea behind these feature extractions stems from the geometric and topological properties of liver imaging. After parameter tuning, the final model comparisons showed that using the two features separately for binary (Acc=63.2%) and grayscale (Acc=64.5%) images for training CNN models, as well as on SVM models using different features—solely esti- mated porosity (Acc=93.3%), solely Euler characteristic numbers (Acc=74.6%), and all features combined (Acc=90.9%). The model using solely estimated porosity as a feature outperformed other models’ overall performance across the four categories, particularly excelling in the mild (A1) category.
Furthermore, we turned to human liver fibrosis classification, utilizing clinical data from 62 patients, including blood tests and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) signal curves. To enhance accuracy, we introduced new features simulating liver properties, such as porosity, diffusion rate, and flow speeds of the portal vein and hepatic artery. Combining these features, we used KNN and Naive Bayes models to achieve excellent results in F0-3 vs. F4-6 and F0-5 vs. F6 classifications, with an overall three-class accuracy of 69.4%. This study underscores the potential value of simulating liver signal concentration models in liver fibrosis assessment, providing biomedical researchers with a deeper understanding and new avenues for research. | en_US |