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Clinical trajectory visualization based on edema quantification from chest x-ray

Key Investigators

  • Ruizhi Liao (MIT)
  • Steve Pieper (ISOMICS)
  • Polina Golland (MIT)

Project Description

We have developed machine learning algorithms to automatically quantify the severity of pulmonary edema from chest x-rays on a continuous scale. The resulting assessment can be used for visualization of heart failure patient recovery trajectories in prior episodes of heart failure to support physicians with a data-driven approach to treating patients. We would like to investigate different ways of visualizing the clinical tracjectories that can inform physicians how different patients responded to different medications/treatment plans.

ML algorithms for quantifying pulmonary edema in chest x-ray: https://www.csail.mit.edu/research/chest-x-ray-analysis

Example x-ray images: Example x-ray images:

Approach

  1. Look into Crossfilter: http://square.github.io/crossfilter/
  2. Look into Vega-lite: https://vega.github.io/vega-lite/
  3. Look into i2b2: https://www.i2b2.org/
  4. ...

Progress

  1. Collected visualization ideas and option from several investigators
  2. Developed a prototyping plan

Next steps

  1. Implement some visualization ideas using a few patient datasets
  2. Iterate with clinical colleagues on best implementation options

Background and References

ML algorithms for quantifying pulmonary edema in chest x-ray: https://www.csail.mit.edu/research/chest-x-ray-analysis