Liver stiffness-platelet ratio index and machine learning models for the noninvasive diagnosis of significant fibrosis in chronic hepatitis B

Figure 1 Study workflow and analytic pipeline. Treatment-naïve chronic hepatitis B patients were screened, eligible participants were enrolled, and fibrosis stages were recorded using Scheuer scoring (including intermediate half-stages). Nonsignificant fibrosis was defined as S0-S1.5 and significant fibrosis as S2-S4. The liver stiffness-platelet ratio index was derived from liver stiffness measurement and platelet count and evaluated alongside conventional noninvasive tests and machine learning/deep learning models using a 70%/30% training/validation split. DL: Deep learning; LPRI: Liver stiffness measurement to platelet ratio index; LSM: Liver stiffness measurement; ML: Machine learning; PLT: Platelet count; T_low/T_high: LPRI thresholds for triage.