Automated detection of dental restorations using deep learning on panoramic radiographs

Abstract

Objectives: Automatically detecting dental conditions using Artificial intelligence (AI) and reporting it visually are now a need for treatment planning and dental health management. This work presents a comprehensive computer-aided detection system to detect dental restorations. Methods: The state of art ten different Deep Learning detection models was used including R-CNN, Faster R-CNN, SSD, YOLOv3 and RetinaNet as detectors. ResNet-50, ResNet-101, XCeption-101, VGG16 and DarkNet53 were integrated as backbone and feature extractor in addition to efficient approaches such Side-Aware Boundary Localization, cascaded structures and simple model frameworks like Libra and Dynamic. Total 684 objects in panoramic radiographs were used to detect available three classes, namely dental restorations, denture and implant. Each model was evaluated by mean average precision (mAP), average recall (AR) and precision-recall curve using Common Objects in Context (COCO) detection evaluation metrics. Results: mAP varied between 0.755 and 0.973 for ten models explored while AR ranges between 0.605 and 0.771. Faster R-CNN RegnetX provided the best detection performance with mAP of 0.973 and AR of 0.771. Area under precision-recall curve was 0.952. Precision-recall curve indicated that errors were mainly dominated by localization confusions. Conclusions: Results showed that the proposed AI-based computer-aided system had great potential with reliable, accurate performance detecting dental restorations, denture and implant in panoramic radiographs. As training models with more data and standardization in reporting, AI-based solutions will be implemented to dental clinics for daily use soon.

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

“…AlexNet has 95.2% which is relatively low. The overall accuracy of the method in this paper is above 95% , which is greatly improved compared with the methods in the current state-of-the-art [15] and [33]- [34].…”

“…The accuracy for Restoration is even more pronounced that showed GoogleNet with better scores. Table Ⅸ summarizes the best results of the different models used in this study and compares them with current state-of-the-art [15] and [33]- [34]. From the results, whether it is for missing teeth or Restoration, this study has better recognition accuracy.…”

Improving resolution of panoramic radiographs: super-resolution concept

“…Deep learning methods have rapidly gained prominence across diverse fields in dentistry due to their remarkable capabilities in data analysis, pattern recognition, and image processing. 9 , 10 , 13 , 31 The combination of these sophisticated algorithms with dental sciences has unveiled new frontiers, offering innovative solutions and augmenting conventional practices in sub-disciplines of dentistry for diagnosis and treatment planning. 8 , 11 , 12 Furthermore, deep learning is a promising technology particularly for super-resolution too, facilitating the reconstruction of high-resolution images from lower-resolution inputs.…”

“…In recent years, artificial intelligence, especially deep neural networks present satisfactory results in dental image analysis. 7–13 As an emerging research field, super-resolution is a technique used in image processing and computer vision to enhance the resolution and quality of an image beyond its original resolution. Deep learning concept has been extensively employed to achieve super-resolution.…”

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

“…Normally, the accurate segmentation and numbering of teeth is difficult and labor-intensive. The task becomes even more complicated when using panoramic dental X-ray images as overlapping boundaries between the teeth pose difficulties in the annotation [ 9 ]. In addition, this procedure is often performed manually, which can be quite time-consuming.…”