Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically—a retrospective study

Abstract

Background The purpose of this investigation was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the accuracy and usefulness of this system for the detection of alveolar bone loss in periapical radiographs in the anterior region of the dental arches. We also aimed to evaluate the usefulness of the system in categorizing the severity of bone loss due to periodontal disease. Method A data set of 1724 intraoral periapical images of upper and lower anterior teeth in 1610 adult patients were retrieved from the ROMEXIS software management system at King Saud bin Abdulaziz University for Health Sciences. Using a combination of pre-trained deep CNN architecture and a self-trained network, the radiographic images were used to determine the optimal CNN algorithm. The diagnostic and predictive accuracy, precision, confusion matrix, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen Kappa, were calculated using the deep CNN algorithm in Python. Results The periapical radiograph dataset was divided randomly into 70% training, 20% validation, and 10% testing datasets. With the deep learning algorithm, the diagnostic accuracy for classifying normal versus disease was 73.0%, and 59% for the classification of the levels of severity of the bone loss. The Model showed a significant difference in the confusion matrix, accuracy, precision, recall, f1-score, MCC and Matthews Correlation Coefficient (MCC), Cohen Kappa, and receiver operating characteristic (ROC), between both the binary and multi-classification models. Conclusion This study revealed that the deep CNN algorithm (VGG-16) was useful to detect alveolar bone loss in periapical radiographs, and has a satisfactory ability to detect the severity of bone loss in teeth. The results suggest that machines can perform better based on the level classification and the captured characteristics of the image diagnosis. With additional optimization of the periodontal dataset, it is expected that a computer-aided detection system can become an effective and efficient procedure for aiding in the detection and staging of periodontal disease.

Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging

“…The total diagnostic accuracies for the alveolar bone levels were reported as 73.04% in the binary classification, and 59.42% in multi-class classification. 50 In 2023, Chen et al developed an ensemble model utilizing the YOLOv5 and VIA labeling platform, including VGG-16 and U-Net architecture, to detect tooth position, tooth shape, periodontal bone level detection, and RBL in periapical and bitewing radiographs. The accuracy of RBL detection was reported to be 97.0%, while the overall accuracy of the model was reported to be approximately 90%.…”

“…The model was based on 13 convolutional layers and two dense layers, and trained using 100 epochs and 16 batch sizes, and the outputs were binary (normal, abnormal) or multi‐class (normal, mild, moderate, severe) categories. The total diagnostic accuracies for the alveolar bone levels were reported as 73.04% in the binary classification, and 59.42% in multi‐class classification 50 . In 2023, Chen et al developed an ensemble model utilizing the YOLOv5 and VIA labeling platform, including VGG‐16 and U‐Net architecture, to detect tooth position, tooth shape, periodontal bone level detection, and RBL in periapical and bitewing radiographs.…”

Optimization of Multi-pressure Condenser Combining Artificial Intelligence and Deep Learning Algorithm

“…According to the different physical properties of the working medium in the condenser, the shell side is divided into three zones: steam zone, air zone and hot well water zone. On the whole, lumped parameter method and energy/mass conservation law are adopted, and considering the difference of pipe diameters in the top of the main condensation zone and some air cooling zones, the heat transfer coefficient is modified [7][8].…”

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis

“… 9 However, research on U-Net models for the segmentation of panoramic radiographs for periodontitis staging in comparison with other deep learning methods remains limited. Nevertheless, several investigations have aimed to detect periodontitis using radiographic images based on deep learning, including Faster R-CNN as a deep learning method for digital panoramic radiographs, 10 a deep convolutional neural network (CNN) algorithm for detecting alveolar bone loss in periapical radiographs, 11 and a deep learning hybrid method for panoramic radiographs. 12 13 …”