Design of an iterative sequential physics-guided and causality-aware ultrasound video framework for robust liver fibrosis classifications

Ultrasound video-based approaches for non-invasive liver fibrosis diagnosis tend to suffer from scanner bias, restricted biomechanical interpretability, and poor cross-site generalizability. The current techniques based on CNN and elastography are less accurate without considering patient-specific parameters like acoustics, segmental physiology, or control of causal confounders affecting diagnosis. This work presents a five-stage pipeline. Acoustic Feature Alignment (AFA) first harmonizes US video to match CT/MR liver volumes through a differentiable acoustic renderer, thereby reducing inter-scanner variability. Hybrid Motion Operator (HMO) fits neural ordinary differential equations to speckle-tracked motion, converting displacements into viscoelastic parameters reflective of fibrosis. Third, the Segmental HoloGraph Transformer (SHGT) fuses biomechanical parameters with vascular structures, producing anatomically coherent fibrosis staging. Fourth, Counterfactual Echo-Pathomics Diffusion (CEPD) disentangles fibrosis-related signatures from acquisition artifacts by generating counterfactual US patches conditioned on histopathology. Finally, TrustCal-ClinBridge (TCCB) delivers calibrated fibrosis indices through conformal prediction, out-of-distribution detection, and laboratory value fusion. This framework promises reliable classification performance, biomechanical interpretability, and validated cross-modality harmonization, providing a path for robust, non-invasive liver fibrosis staging from US video.•Five-stage sequential pipeline integrating acoustics, physiology, causal modeling, and calibration.•Counterfactual diffusion separates fibrosis-specific signals from acquisition artifacts.•Clinically calibrated fibrosis indices ensure interpretability and cross-site reliability.