This repo contains implementation of the deep learning-based outcome prediction model used for osteoarthritis research as described in our paper: Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative. By using this implementation, you can either train new models using nested cross-validation or obtain TKR outcome and KL grade predictions by using our pretrained models.

1. Please refer to requirements.txt to install all dependencies for this project.
2. Check Extract L&R Knee Radiographs section to extract and save left and right knee images from bilateral posterioranterior(PA) fixed-flexion knee radiographs of the patients to the ./data folder automatically.
3. Download pretrained torchvision ResNet-34 model from here and save it inside the main folder.
4. Once you have data ready, you can use train_TL_nestedCV_strata.py to train a model with seven-fold nested cross-validation. Trained models and inference results will be saved within the folder named 'model_weights_multiTask...'
5. Use inference.py file to obtain TKR outcome and KL grade predictions by using our pretrained model. Please see Inference section for details.

• 728_Cohort_KLG_w_Strata.csv file includes subject IDs for each patient who are included in the study. Seperate columns identify: TKR status of patients (0: controls, 1:patients underwent TKR within 9 years from baseline), knee side (0: Left Knee, 1: Right knee), KL grade from the patient's knee and strata (from case control matching) for each patient included in the study.
• ./data: Folder for left or right knee radiographs of the patients that will be used for training. Once the ./ExtractKnee/preprocessing.py file is executed this folder will be populated by radiographs defined in ./ExtractKnee/output_00m.csv file .
• ./TestSets: contains information about subjects who are included seven seperate groups for nested cross-validation used in this study. Filenames match with the "Test Set Numbers" defined in Table 5 of the paper.
• ./ModelWeights: Trained model weights used in this study. They can be downloaded from here.
• ./ExtractKnee: contains the code and .csv files to extract single knee images from bilateral PA fixed-flexion knee radiographs

When you have an annotation .csv file, this code enables you to extract knee joint images from bilateral posteroanterior (PA) fixed-flexion knee radiographs in the OAI dataset.output_00m.csv file was used to extract knee joint images that we used for the paper. The annotation file looks like

file_path,pred_bbox
0.C.2/9003380/20041206/00429603/001,"[0.58988559 0.24353482 0.86547155 0.57789375 0.14393548 0.25309042 0.41856452 0.5861953 ]"
0.E.1/9004175/20050503/00787104/001,"[0.65715023 0.29639241 0.94106405 0.64110759 0.08047989 0.30884113 0.36594869 0.65544459]"

Column file_path is where radiographs are stored as a DICOM file. The second column pred_bbox are the coordinates of bounding boxes for left and right knees. In the knee joint extraction code, we used regular expression to parse this string. Note that these coordinates format is different compared to the original file used in previous studies. The coordinates are saved as a float number between 0 to 1 defining the ratio of pixel locations with respect to image size instead of exact coordinates within the image.

You can use preprocessing.py to generate right and left knee images of size knee joint area with size 1024×1024 in HDF5 file format from bilateral PA fixed-flexion knee radiograph DICOMs. Example use would be:

# this code will use 'output_00m.csv' to generate single knee image dataset.
python preprocessing.py -m 00m --content ./output_00m.csv --output-dir ../data/

-m sets the sub-folder name for where you want to save HDF5 files. --content gives path for annotation files. --output-dir is the folder where you want to save images with the subfolder from -m.

You can use this repo to train models for predicting TKR outcome and KL grade. Default parameters are defined within train_TL_nestedCV_strata.py file. So, you directly run the following script to train DL models with nested cross-validation.

python train_TL_nestedCV_strata.py

Please see the arguments in the .py file to make input argument changes if you need.

Once you download pretrained model weights from here to the ./ModelWeights folder, you can use the following script to obtain predictions of TKR and KL grade for a specific single knee radiograph (e.g. for the left knee of patient with ID=9011918):

python inference.py --filename ./data/00m/9011918_00m_LEFT_KNEE.hdf5

The output of this script will provide the predictions of TKR and KL grades as shown below.

--- Inference Results ---
Predictions for ** 9011918_00m_LEFT_KNEE.hdf5 **
Total Knee Replacement (TKR): 0.95
KL grade 0: 0.00
KL grade 1: 0.01
KL grade 2: 0.02
KL grade 3: 0.92
KL grade 4: 0.04

This repository is licensed under the terms of the GNU AGPLv3 license.

If you found this code useful, please cite our paper:

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative Kevin Leung, Bofei Zhang, Jimin Tan, Yiqiu Shen, Krzysztof J. Geras, James S. Babb, Kyunghyun Cho, Gregory Chang, Cem M. Deniz Radiology 2020

@article{leung2020osteoarthritis,
    title = {Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative},
    author = {Kevin Leung and Bofei Zhang and Jimin Tan and Yiqiu Shen and Krzysztof J. Geras and James S. Babb and Kyunghyun Cho and Gregory Chang and Cem M. Deniz}, 
    journal = {Radiology},
    year = {2020},
    doi = {10.1148/radiol.2020192091},
    URL = {https://doi.org/10.1148/radiol.2020192091}
}