Source code accompanying the ISBI 2022 publication [paper] [arXiv preprint].

See also our multimodal extension of this work - https://github.com/imedslab/OAProgressionMMF .

1. The code requires the OAI Baseline (i.e. 00m) dataset - textual variables and SAG 3D DESS MR images.

2. Create a Conda environment from environment.yml. Install the code as a Python module.

3. See entry/runner.sh for the complete workflow.

4. The structure of the original project is as follows:

   ./project/
     | data/  # preprocessed scans and variables
       | OAI_Clin_prep/
         | meta_base.csv
       | OAI_SAG_3D_DESS_prep/
       | OAI_XR_PA_prep/
     | src/ (this repository)
     | results/  # model weights, intermediate and final results 
       | experiment_0/
         | weights/
         | ...
       | experiment_1/
       | ...
   

This code is freely available for research purposes.

The software has not been certified as a medical device and, therefore, must not be used for diagnostic purposes in a real clinical scenario.

@inproceedings{panfilov2022predicting,
  title={Predicting Knee Osteoarthritis Progression from Structural {MRI} Using Deep Learning},
  author={Panfilov, Egor and Saarakkala, Simo and Nieminen, Miika T and Tiulpin, Aleksei},
  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}