In this study, we evaluated the diagnostic performance of a CNN-based model VGG-16 to detect periodontal bone loss and classify the alveolar bone levels in teeth affected by periodontal disease. The presence of alveolar bone loss was detected with high accuracy by the system. Our findings revealed that deep CNN had a diagnostic accuracy of 73.04% in detecting an alveolar bone loss in the anterior teeth of both arches. The total diagnostic accuracy for the multi-classification was 59.42%, with the highest diagnostic accuracy for the presence of normal alveolar bone, followed by mild, moderate, and severe alveolar bone loss.
Our findings are supported by Lee et al.  who developed a deep learning model to classify periodontally compromised posterior teeth from periapical radiographs. They revealed a diagnostic accuracy of 81% for periodontally compromised premolar teeth and 76.7% for molars, similar to our findings of 73% for the anterior teeth. Another study by Lee Chun demonstrated a diagnostic accuracy of 0.85 and no significant difference in the RBL percentage measurements determined by the DL and examiners and high sensitivity, specificity, and accuracy for the different stages, all over 0.8 . Other models used DL to detect RBL or calculate the RBL percentage from the panoramic radiographs and to assign periodontitis staging from the panoramic radiographs [7,8,9,10,11,12,13,14,15,16]. Although these models on the panoramic radiographs had good accuracy and reliability in assessing the bone level on the panoramic radiographs, it is generally not recommended to rely on panoramic radiographs due to the presence of distorted images, overlapping objects, and low resolution [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18, 21].
The proposed model is the first model that assigned a severity category based on the traditional classification by the International Workshop for Classification of Periodontal Diseases and Conditions (1999) with the severity of the bone loss has been characterized as mild with bone loss is in the coronal third of the root, moderate with bone loss is in the middle third of the root and advanced when in the apical third of the root length . Healthy indicated no vertical or horizontal alveolar bone loss.
The current study was based on periapical radiographs, the standard radiographic images for periodontal diagnosis. A major highlight of our study is that we have not excluded caries, restorations, and endodontically treated teeth to increase the complexity and simulate real-life scenarios as much as possible. The current results confirmed the CNN model, designed to detect the presence and absence of RBL and categorize the bone loss based on the severity, may assist in saving time and produce confirmatory decisions for RBL detection and categorization of the severity. The implication is that the clinicians would not have to assign a bone loss stage by manually calculating the RBL percentage for each tooth, a very time-consuming process.
The deep CNN algorithm yielded promising results in various fields of medicine, including imaging [4,5,6,7,8,9,10,11,12,13,14,15]. In our study, we performed supervised deep learning using a CNN-based model called VGG-16 (Visual Geometry Group) on a periapical radiographic dental dataset. We confirmed that the results had a close predictive accuracy compared with the periodontists. The deep CNN algorithm had a higher diagnostic accuracy to distinguish between no bone loss from bone loss in the upper and lower incisor teeth and the deep CNN algorithm was better trained and optimized for the detection of a normal PCT. However, the accuracy of categorizing the severity was lower. The diagnostic accuracy for severe bone loss was the lowest overall, and the trained deep CNN algorithm was poorly optimized for the detection of severe bone loss. Further studies on the mechanisms underlying deep CNN algorithms are necessary. The sensitivity, specificity, and the F-measure demonstrated acceptable performance for the binary classification, which supports the appropriateness of this method. Other topics for future research include additional techniques for improving the system output, such as applying more advanced augmentation techniques, extending the dataset, and using more recent CNN architectures.
In summary, this study found that a faster R-CNN trained on a limited number of labeled imaging data had a good capability of detecting the presence of bone loss and a satisfactory detection ability for the severity of bone loss in teeth. The application of a faster R-CNN to assist in detecting bone loss may reduce diagnostic effort by saving assessment time and automating screening documentation. A good quality tooth edge is important if the periodontal tissue is damaged in order to increase the performance accuracy of a PCT model, both diagnostically and predicatively. The Deep CNN algorithm can automatically extract features from PCT images and identify diverse characteristics in the input image, such as spots, corners, edges, or progressively complex characteristics, including patterns, structures, and shapes . The VGG-16 has a powerful advantage in deciphering the detection problem, supporting its use in the present study .
The strengths of our study include the fact the consideration of imbalanced data, for which accuracy may not the best measure of the performance of a classifier. We considered other measures such as precision and recall (also known as sensitivity) as the most appropriate measure for the performance of imbalanced data.