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Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs

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Abstract

Purpose

Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren–Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs from CT images.

Methods

Automatic grading of the hip OA severity was performed using deep learning-based models. The models were trained to predict the disease grade using two grading schemes, i.e., predicting the Crowe and KL grades separately, and predicting a new ordinal label combining both grades and representing the disease progression of hip OA. The models were trained in classification and regression settings. In addition, the model uncertainty was estimated and validated as a predictor of classification accuracy. The models were trained and validated on a database of 197 hip OA patients, and externally validated on 52 patients. The model accuracy was evaluated using exact class accuracy (ECA), one-neighbor class accuracy (ONCA), and balanced accuracy.

Results

The deep learning models produced a comparable accuracy of approximately 0.65 (ECA) and 0.95 (ONCA) in the classification and regression settings. The model uncertainty was significantly larger in cases with large classification errors (\(P< 6\text {e}{-}3\)).

Conclusions

In this study, an automatic approach for grading hip OA severity from CT images was developed. The models have shown comparable performance with high ONCA, which facilitates automated grading in large-scale CT databases and indicates the potential for further disease progression analysis. Classification accuracy was correlated with the model uncertainty, which would allow for the prediction of classification errors. The code will be made publicly available at https://github.com/NAIST-ICB/HipOA-Grading.

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Acknowledgements

This work was funded by MEXT/JSPS KAKENHI (19H01176, 20H04550, 21K16655, 21K18080).

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Authors and Affiliations

  1. Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan

    Masachika Masuda, Mazen Soufi, Yoshito Otake, Yi Gu & Yoshinobu Sato

  2. Department of Orthopaedics, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan

    Keisuke Uemura, Sotaro Kono, Kazuma Takashima & Seiji Okada

  3. Department of Orthopaedic Medical Engineering, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan

    Hidetoshi Hamada & Nobuhiko Sugano

  4. Department of Bone and Joint Surgery, Graduate School of Medicine, Ehime University, Toon, Ehime, Japan

    Masaki Takao

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  1. Masachika Masuda
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Correspondence to Masachika Masuda, Mazen Soufi or Yoshinobu Sato.

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Ethical approval was obtained from the Institutional Review Boards (IRBs) of the institutions participating in this study (IRB approval numbers: 21115 for Osaka University Hospital and 2020-M-7 for Nara Institute of Science and Technology.)

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Masuda, M., Soufi, M., Otake, Y. et al. Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03087-1

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