基於電子健康記錄，預測病人胸部X光影像的時間變化。

Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records

September 11, 2024

作者: Daeun Kyung, Junu Kim, Tackeun Kim, Edward Choi

cs.AI

摘要

胸部X光攝影（CXR）是醫院中用於評估患者狀況並隨時間監測變化的重要診斷工具。生成模型，特別是基於擴散的模型，已顯示出在生成逼真的合成X光方面具有潛力。然而，這些模型主要集中於使用單一時間點數據進行有條件生成，即通常在特定時間採取的CXR及其相應報告，限制了其臨床效用，特別是用於捕捉時間變化。為解決這一限制，我們提出了一個新的框架，名為EHRXDiff，通過整合先前的CXR和隨後的醫療事件，例如處方、實驗室測量等，來預測未來的CXR影像。我們的框架根據潛在擴散模型，條件是先前的CXR影像和醫療事件歷史，動態跟踪並預測疾病進展。我們全面評估我們的框架在三個關鍵方面的表現，包括臨床一致性、人口統計一致性和視覺逼真性。我們展示了我們的框架生成了高質量、逼真的未來影像，捕捉潛在的時間變化，表明其作為臨床模擬工具進一步發展的潛力。這對於醫療領域的患者監測和治療計劃提供了寶貴的見解。

English

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.