Building a deep learning model to detect osteoporosis from dental panoramic X-Ray image (Preprint)
Authors: Brangakgi Farah , Khadra Yasser 
Published: July 2024

BACKGROUND

The project discusses the development of a deep learning model to detect osteoporosis from dental panoramic X-Ray images. It provides an in-depth understanding of human bone structure, osteoporosis, its symptoms, causes, prevalence, and risk factors. The project also explains bone density measurement using dual-energy X-ray absorptiometry (DEXA) and the application of artificial intelligence (AI) and machine learning (ML) in medical imaging. The study uses panoramic dental X-rays to evaluate AI technology in dental imaging and classification of mandible inferior cortical based on Klemetti and Kolmakow criteria. The model architecture consists of convolutional, pooling, fully connected, ReLU, and Softmax layers. Dropout and earlystop is added to the model. The training process uses the train-test approach with 100 epochs and a batch size of 32, and performance evaluation measures such as accuracy, sensitivity, specificity, and F1-score are used to assess the classifier's performance. The findings and methodology provide a comprehensive understanding of the application of deep learning in the detection of osteoporosis from dental panoramic X-Ray images, and the study demonstrates a robust approach to implementing AI in medical imaging for osteoporosis detection.

OBJECTIVE

• A tool for assisting dentists was developed in this study in identifying signs of osteoporosis for future patient care or aiding orthopedic physicians in taking actions upon diagnosing panoramic dental images.
• This tool can also facilitate individuals in recognizing indicators in their own dental panoramic images, promoting increased attention to their health or consulting a doctor to mitigate the progression of osteoporosis.
• This tool holds significant medical and economic importance, as doctors can diagnose osteoporosis from panoramic images, saving costs and time for patients.

METHODS

The Python model utilizes several essential libraries for various tasks. NumPy facilitates numerical operations and efficient handling of data arrays. The OS module interacts with the operating system, aiding in file and directory manipulation. The Glob module searches for pathnames matching specified patterns. OpenCV and PIL assist in image processing tasks, such as resizing and manipulation. TensorFlow's Keras module offers utilities for preparing images for deep learning models. Provided code snippets illustrate functions for renaming images to numerical formats, resizing images, and constructing a model with MobileNet architecture. Early stopping is implemented during training to prevent overfitting, monitoring the model's performance on a validation set to halt training if no improvement is observed after a set number of epochs. Training results shows duration, loss, and accuracy metrics for each epoch.

RESULTS

Fig. 2 presents a segment of the training outcomes, exhibiting information for every epoch, encompassing the duration, training loss, validation loss, and both training and validation accuracy. Fig. 3 depicts a graph illustrating the accuracy throughout training and validation across epochs.
The model demonstrates an overall improvement in accuracy over the epochs. Fluctuations in validation accuracy suggest some sensitivity to the dataset or potential overfitting.
Table 1 shows the overall experimental results achieved by the proposed method on the proposed features using MobileNet classifier. In summary, the model demonstrates good performance with high precision, recall, and F1-score, resulting in an overall accuracy of 85%.
A separate set of data, which was not used during the model building process, was introduced to evaluate the performance of the model. This evaluation dataset consisted of dental panoramic X-ray images from patients who had undergone both DXA scan and DBR(26). The labeling of the evaluation dataset was based on the DXA scan results. Specifically, the dataset included 10 normal images and 19 osteoporosis images. The model was then tested on this evaluation dataset, and the results shows that the overall count of true incidences is 23 out of 29. Consequently, the accuracy is 79.3%, which is really close to the model evaluation.

CONCLUSIONS

The project discusses the development of a deep learning model to detect osteoporosis from dental panoramic X-ray images. The project highlights the importance of early detection and treatment of osteoporosis and the potential of artificial intelligence (AI) and deep learning models in improving diagnostic accuracy. The findings contribute to the advancement of AI in healthcare. The project explains the working principles of convolutional neural networks (CNNs) in image processing and sequence prediction tasks. It uses precision, recall, F1-score and accuracy in the evaluation of the proposed deep learning model. The results section presents the performance evaluation of the CNN model using dental panoramic X-ray images. It also shows the experimental results achieved by the proposed method. The project also mentions the calculation of accuracy, sensitivity, and specificity scores for evaluating the model's performance. In terms of the procedure, the project briefly describes the process of obtaining dental panoramic X-ray images and the positioning of the patient during the scan. It also mentions the signs observed in the dental panoramic X-ray images that indicate osteoporosis, such as thinning of the bone cortical and reduced definition of the cortical bone. For future research, future research is needed, using different deep CNN architectures, more validated and qualified labeled image dataset, the appropriate number of datasets, since the method has shown a great potential for assessing a large number of images.