Towards Predicting Temporal Changes in a Patient's Chest X-ray Images
  based on Electronic Health Records
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.
66e3bf06dba1e4fee4b33434
Daeun Kyung
admin_assigned
2024-09-17T11:33:57.894Z
645cd00f5ebf379fd6d7a4c1
/avatars/be8375ed32ac234919e26a2450bf9d38.svg
Daeun Kyung
user
dek924
66e3bf06dba1e4fee4b33435
Junu Kim
admin_assigned
2024-09-17T11:34:03.399Z
64675818a9b4610868a134a5
https://cdn-avatars.huggingface.co/v1/production/uploads/64675818a9b4610868a134a5/BGCRgg0oP1S2aSHXquZdm.jpeg
Junu Kim
user
starmpcc
66e3bf06dba1e4fee4b33436
Tackeun Kim
66e3bf06dba1e4fee4b33437
Edward Choi
admin_assigned
2024-09-17T11:34:29.235Z
65e8fb912dcdb39d1934260c
/avatars/358504b57f2a600753e9bc459091061b.svg
Edward Choi
user
forgetnight
Towards Predicting Temporal Changes in a Patient's Chest X-ray Images
  based on Electronic Health Records
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.
EHRXDiff uses a latent diffusion model to generate future CXR images by integrating past CXR data and medical events, enhancing clinical utility by capturing temporal disease progression.
diffusion-based models latent diffusion model EHRXDiff clinical consistency demographic consistency visual realism future CXR images temporal changes patient monitoring treatment planning