Interference-Free Causality Learning Promotes Cross-Level, Fine-Grained Diagnosis of Coronary Artery Disease in Coronary CT Angiography.

Abstract

With the growing global threat of coronary artery disease (CAD), automated CAD diagnosis techniques based on coronary CT angiography (CCTA) have been developed. However, their clinical applicability remains limited due to the heterogeneity of stenosis and plaque attributes, as well as confounders within the causal relationships of CAD diagnosis. This work introduces the Attribute-Decoupled Intervention Network (ADI-Net), a confounder-free CAD diagnosis framework designed for fine-grained analysis at both the artery and patient levels, aligning with real-world clinical practice. ADI-Net employs an attribute-decoupled representation that effectively captures the heterogeneous features of stenosis and plaque with differential constraints, enabling precise, fine-grained classification. Additionally, the dynamic-updating causal intervention continuously refines confounder banks and applies the Do-expression within a complete causality, ensuring comprehensive, cross-level assessments. Experiments on CCTA datasets from three clinical centers demonstrate that ADI-Net outperforms state-of-the-art methods in cross-level, fine-grained CAD diagnosis, exhibiting superior robustness, domain adaptability, and data efficiency.