基于多模态 超 声 成 像 数 据 的 慢 性 肝 病 肝 纤 维 化、 炎 症 和 脂 肪 变性的智能分级诊断

Objective To develop a non-invasive, accurate, convenient, and widely applicable intelligent diagnostic system to diagnose simultaneously liver fibrosis, inflammation, and steatosis of chronic liver disease (CLD). Methods This study is based on multimodal ultrasound imaging data from CLD patients, including two-dimensional B-mode ultrasound images, two-dimensional shear wave elastography, transient elastography data, and the corresponding original radio-frequency data. Quantitative ultrasound methods were used to extract multimodal features from these multimodal data, and the results of ultrasound-guided liver biopsy were used as the gold standard. Support vector machine (SVM) was used to construct an intelligent grading diagnosis system for CLD in a binary-classification manner. Results The proposed method achieves high the receiver operating characteristic (ROC) area under the curve (AUC) of 0. 81, 0. 80, 0. 89, 0. 87 for the classification of fibrosis grade ≥F1, ≥F2, ≥F3 ≥F4, and 0. 80, 0. 93, 0. 93 for inflammation ≥A2, ≥A3, ≥A4, and 0. 75, 0. 92 for steatosis ≥S1, ≥ S2. Conclusion The results indicated that the proposed method showed potential expected to be promoted to clinical applications.