## 2 Materials and Methods
### Machine Learning and Deep Learning Algorithms  
Multiple panoramic imaging devices were used to create the original images. The images were randomly distributed into a train, test, and validation set (60%, 20%, 20%). Next, we resized (224 \(\times\) 224) all dataset images and applied data augmentation techniques to the train dataset images (rotation of \(\pm\)30 degrees, horizontal flipping, 20-80% brightness). Image augmentation was used to reduce overfitting and improve generalization. A surgeon manually defined the region of interest by delineating the maxilla and the sinuses. Multiple supervised pre-trained machine learning algorithms (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), as well as a customized CNN model, were used to evaluate the algorithms' ability to predict the clinical classification systems RS and Archer. The pre-trained models were used for transfer learning. In transfer learning, all relevant information is gathered, and this knowledge is subsequently transferred to enable solutions to other problems. This way, the panoramic radiography dataset can be used as a basis for a deep learning model with pretrained weights. The models were frozen in a way that we used the basic models and made changes to the final layer only, as these models were designed to handle multiple classes, whereas we needed to solve a binary classification problem. For this, we made the layer non-trainable and built a last fully connected layer. Overall, we flattened our base model's output to 1 dimension, added a fully connected layer with hidden units and ReLU activation, used a dropout rate, and added a final Fully Connected Sigmoid Layer. The specific characteristics of the models, including each layer, are shown in the code available in the data availability section. We used the RMSProp Optimiser (custom made CNN), Adam optimizer (VGG16, InceptionV2, EfficientNet, MobileNetV2), or SGD optimizer (ResNet50) with binary cross-entropy for loss evaluation. We applied learning rate decay with a minimum learning rate of 1 \(\times\) 10\({}^{-4}\), a factor of 0.5, while monitoring validation accuracy and early stopping (5 epochs). Steps per epoch were calculated as the sample size for the training set divided (using the integer division operator) by the batch size, where the batch size was 10. Models were trained for 10 epochs. We did not use a grid search, random search, or Bayesian optimization for hyperparameter tuning, but we used a manual search to adjust the parameters until the best metrics were obtained. Grid search and manual search are the most widely used strategies for hyperparameter optimization [20]. Hyperparameter tuning using fine-tuning algorithms was intended to be applied to improve models more precisely in the case where an AUC over 0.75 could be reached for any model. In case no evidence was found that models were suitable to reach higher accuracies, we decided not to perform further hyper parameter tunings in a resource-oriented way, as these fine-tuning techniques are more intended to build precise models to classification tasks rather than exploring the feasibility/exploratory approach of whether a reliable classification is possible or not. The other presented algorithms (custom-made CNN, BoVW approach, MLP, RBNN) were trained exclusively on our dataset without pre-trained weights. The algorithms' structure and the code are available in the data availability section. AUC and accuracy were calculated to evaluate each model's performance. AUC represents the area under the receiver operating characteristic curve (ROC). Accuracy is a metric used in classification problems to determine the percentage of correct predictions. Statistical analyses were conducted in Python and SPSS v26 (IBM, Armonk, NY, USA). Algorithms were built and evaluated in Python using the OpenCV, NumPy, Pillow, Seaborn, Matplotlib, TensorFlow, Keras, and scikit-learn libraries.