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

Area of Science:

• Medical imaging analysis
• Causal inference in healthcare
• Biomedical data science

Background:

• Understanding disease progression and organ impact is vital for clinical outcomes.
• Existing methods struggle with patient heterogeneity and complex causal pathways.

Purpose of the Study:

• Introduce a novel causal inference framework, Heterogeneous Causal Disease Pattern Detection (HCDPD).
• Map causal pathways from early disease to organ manifestation in medical images.
• Address patient heterogeneity in disease pattern analysis.

Main Methods:

• Developed the Heterogeneous Causal Disease Pattern Detection (HCDPD) framework.
• Utilized advanced Bayesian inference techniques for causal effect estimation.
• Applied the framework to the Osteoarthritis Initiative (OAI) dataset.

Main Results:

• Successfully identified and delineated diverse disease patterns in patients.
• Estimated direct and indirect causal effects within the HCDPD framework.
• Demonstrated HCDPD's effectiveness in analyzing heterogeneous patient data.

Conclusions:

• HCDPD provides critical insights into disease-organ causal relationships.
• The framework supports early intervention and personalized treatment strategies.
• HCDPD advances causal inference applications in medical imaging research.