## 2 Materials and Methods
### Deep Learning Model
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**ONFH (Right Hip) Diagnosis Model**} & \multicolumn{2}{c}{**ONFH (Left Hip) Diagnosis Model**} \\ \hline \multirow{4}{*}{Sample size (patients)} & 213 for training (69.8\%), 64 for validation (21.0\%), and 28 for test (9.2\%) & 213 for training (69.8\%), 64 for validation (21.0\%), and 28 for test (9.2\%) \\ \cline{2-3}  & Sample ratio: normal 74.2\%, ONFH 25.8\% & Sample ratio: normal 79.8\%, ONFH 20.2\% \\ \hline \multirow{4}{*}{CNN model} & EfficientNetV2B2 with fine-tuning (Unfreeze top 3 layers) [18] & Xception with full training (Unfreeze all layers) [19] \\ \cline{2-3}  & RMSProp optimizer, ReLU activation & RMSProp optimizer, ReLU activation \\ \cline{2-3}  & Learning rate: \(1\times 10^{-5}\); batch size: 32 & Learning rate: \(1\times 10^{-5}\); batch size: 32 \\ \cline{2-3}  & Data augmentation (rotation range: \(10^{\circ}\); zoom range 10\%) & Data augmentation (rotation range: \(10^{\circ}\); zoom range: \(10\%\)) \\ \cline{2-3}  & Dropout for regularization & Dropout for regularization \\ \cline{2-3}  & ROI image (500 \(\times\) 500) resized to 260 \(\times\) 260 & ROI image (500 \(\times\) 500) resized to 299 \(\times\) 299 \\ \hline \multirow{4}{*}{Model performance} & Accuracy: 100\% for training data & Accuracy: 98.6\% for training data \\ \cline{2-3}  & Training AUC: 1.000 & Training AUC: 0.999 with 95\% CI [0.997–1.000] \\ \cline{2-3}  & Validation accuracy: 93.8\% for validation data & Validation accuracy: 92.2\% for validation data \\ \cline{2-3}  & Validation AUC: 0.927 with & Validation AUC: 0.893 with 95\% CI [0.853–1.000] \\ \cline{2-3}  & Test accuracy: 89.3\% for test data & Test accuracy: 92.9\% for test data \\ \cline{2-3}  & Test AUC: 0.912 with 95\% CI [0.773–1.000] & Test AUC: 0.902 with 95\% CI [0.747–1.000] \\ \hline \hline \end{tabular} \begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{ONFH (Right Hip) Diagnosis Model} & \multicolumn{2}{c}{**ONFH (Left Hip) Diagnosis Model**} \\ \hline \multirow{4}{*}{Sample size (patients)} & 213 for training (69.8\%), 64 for validation (21.0\%), and 28 for test (9.2\%) & 213 for training (69.8\%), 64 for validation (21.0\%), and 28 for test (9.2\%) \\ \cline{2-3}  & Sample ratio: normal 74.2\%, ONFH 25.8\% & Sample ratio: normal 79.8\%, ONFH 20.2\% \\ \hline \multirow{4}{*}{CNN model} & EfficientNetV2B2 with fine-tuning (Unfreeze top 3 layers) [18] & Xception with full training (Unfreeze all layers) [19] \\ \cline{2-3}  & RMSProp optimizer, ReLU activation & RMSProp optimizer, ReLU activation \\ \cline{2-3}  & Learning rate: \(1\times 10^{-5}\); batch size: 32 & Learning rate: \(1\times 10^{-5}\); batch size: 32 \\ \cline{2-3}  & Data augmentation (rotation range: \(10^{\circ}\); zoom range 10\%) & Data augmentation (rotation range: \(10^{\circ}\); zoom range: \(10\%\)) \\ \cline{2-3}  & Dropout for regularization & Dropout for regularization \\ \cline{2-3}  & ROI image (500 \(\times\) 500) resized to 260 \(\times\) 260 & ROI image (500 \(\times\) 500) resized to 299 \(\times\) 299 \\ \hline \multirow{4}{*}{Model performance} & Accuracy: 100\% for training data & Accuracy: 98.6\% for training data \\ \cline{2-3}  & Training AUC: 1.000 & Training AUC: 0.999 with 95\% CI [0.997–1.000] \\ \cline{2-3}  & Validation accuracy: 93.8\% for validation data & Validation accuracy: 92.2\% for validation data \\ \cline{2-3}  & Validation AUC: 0.927 with & Validation AUC: 0.893 with 95\% CI [0.853–1.000] \\ \cline{2-3}  & 95\% CI [0.853–1.000] & 95\% CI [0.784–1.000] \\ \cline{2-3}  & Test accuracy: 89.3\% for test data & Test accuracy: 92.9\% for test data \\ \cline{2-3}  & Test AUC: 0.912 with 95\% CI [0.773–1.000] & Test AUC: 0.902 with 95\% CI [0.747–1.000] \\ \hline \hline \end{tabular} \
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{ONFH (Right Hip) Diagnosis Model} & \multicolumn{2}{c}{**ONFH (Left Hip) Diagnosis Model**} \\ \hline \multirow{4}{*}{Sample size (patients)} & 213 for training (69.8\%), 64 for validation (21.0\%), and 28 for test (9.2\%) & 213 for training (69.8\%), 64 for validation (21.0\%), and 28 for test (9.2\%) \\ \cline{2-3}  & Sample ratio: normal 74.2\%, ONFH 25.8\% & Sample ratio: normal 79.8\%, ONFH 20.2\% \\ \hline \multirow{4}{*}{CNN model} & EfficientNetV2B2 with fine-tuning (Unfreeze top 3 layers) [18] & Xception with full training (Unfreeze all layers) [19] \\ \cline{2-3}  & RMSProp optimizer, ReLU activation & RMSProp optimizer, ReLU activation \\ \cline{2-3}  & Learning rate: \(1\times 10^{-5}\); batch size: 32 & Learning rate: \(1\times 10^{-5}\); batch size: 32 \\ \cline{2-3}  & Data augmentation (rotation range: \(10^{\circ}\); zoom range 10\%) & Data augmentation (rotation range: \(10^{\circ}\); zoom range: \(10\%\)) \\ \cline{2-3}  & Dropout for regularization & Dropout for regularization \\ \cline{2-3}  & ROI image (500 \(\times\) 500) resized to 260 \(\times\) 260 & ROI image (500 \(\times\) 500) resized to 299 \(\times\) 299 \\ \hline \multirow{4}{*}{Model performance} & Accuracy: 100\% for training data & Accuracy: 98.6\% for training data \\ \cline{2-3}  & Training AUC: 1.000 & Training AUC: 0.999 with 95\% CI [0.997–1.000] \\ \cline{2-3}  & Validation accuracy: 93.8\% for validation data & Validation accuracy: 92.2\% for validation data \\ \cline{2-3}  & Validation AUC: 0.927 with & Validation AUC: 0.893 with 95\% CI [0.853–1.000] \\ \cline{2-3}  & 95\% CI [0.853–1.000] & 95\% CI [0.784–1.000] \\ \cline{2-3}  & Test accuracy: 89.3\% for test data & Test accuracy: 92.9\% for test data \\ \cline{2-3}  & Test AUC: 0.912 with 95\% CI [0.773–1.000] & Test AUC: 0.902 with 95\% CI [0.747–1.000] \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{ONFH, osteonecrosis of the femoral head; CNN, convolutional neural network; ROI, region of interest; AUC, area under the curve; CI, confidence interval.} \\ \end{tabular}
\end{table}