Automatic hip osteoarthritis grading with uncertainty estimation from
  computed tomography using digitally-reconstructed radiographs
Masachika Masuda Mazen Soufi Yoshito Otake Keisuke Uemura Sotaro Kono Kazuma Takashima Hidetoshi Hamada Yi Gu Masaki Takao Seiji Okada Nobuhiko Sugano Yoshinobu Sato
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
(DRRs) from CT images. 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.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<6e-3). 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.