HCDPD: A Heterogeneous Causal Framework for Disease Pattern Detection in Medical Imaging

Understanding the causal effects of diseases on body organs through medical imaging is crucial for advancing research and improving clinical outcomes. This paper introduces a novel causal inference framework, Heterogeneous Causal Disease Pattern Detection (HCDPD), designed to map the complex causal pathways from early-stage diseases to latent disease patterns and their manifestation in organs as observed in later-stage medical images. HCDPD serves as a potential outcome framework for multivariate responses. It is particularly valuable in scenarios where patients exhibit significant heterogeneity, while normal controls remain relatively homogeneous. Through the application of advanced Bayesian inference techniques, our method effectively estimates both direct and indirect causal effects within the HCDPD framework. We applied HCDPD to the Osteoarthritis Initiative (OAI) dataset, successfully identifying and delineating diverse disease patterns across different patients. This capability provides critical insights that can inform early interventions and tailor personalized treatment strategies in clinical practice.