Towards Predicting Temporal Changes in a Patient's Chest X-ray Images
  based on Electronic Health Records
Authors: name: Daeun Kyung, name: Junu Kim, name: Tackeun Kim, name: Edward Choi
Organization: KAIST AI

Chest X-ray imaging (CXR) is an important diagnostic tool used in hospitals
to assess patient conditions and monitor changes over time. Generative models,
specifically diffusion-based models, have shown promise in generating realistic
synthetic X-rays. However, these models mainly focus on conditional generation
using single-time-point data, i.e., typically CXRs taken at a specific time
with their corresponding reports, limiting their clinical utility, particularly
for capturing temporal changes. To address this limitation, we propose a novel
framework, EHRXDiff, which predicts future CXR images by integrating previous
CXRs with subsequent medical events, e.g., prescriptions, lab measures, etc.
Our framework dynamically tracks and predicts disease progression based on a
latent diffusion model, conditioned on the previous CXR image and a history of
medical events. We comprehensively evaluate the performance of our framework
across three key aspects, including clinical consistency, demographic
consistency, and visual realism. We demonstrate that our framework generates
high-quality, realistic future images that capture potential temporal changes,
suggesting its potential for further development as a clinical simulation tool.
This could offer valuable insights for patient monitoring and treatment
planning in the medical field.