Dental panoramic radiographs are often obtained at dental clinic visits for diagnosis and recording purposes. Automated filing of dental charts can help dentists in reducing their workload and improving diagnostic efficiency. The purpose of this study is to develop a system that prerecords a dental chart by recognizing teeth with their numbers and restoration history on dental panoramic radiographs. The proposed system uses YOLO which detects 16 types of teeth and restoration conditions simultaneously. Based on the detected tooth types, they were further classified into 32 types and combined with the tooth conditions by post-processing. We tested our method on 870 panoramic images obtained at 10 different facilities by 5-fold cross validation. The proposed method obtained 0.99 recall and precision for recognition of 32 tooth types and 0.90 recall and 0.90 precision on determining the tooth condition. It has the potential to assist prefiling the dental charts for efficient dental care.
Purpose: The purpose of our study was to analyze dental panoramic radiographs and contribute to dentists’ diagnosis by automatically extracting the information necessary for reading them. As the initial step, we detected teeth and classified their tooth types in this study.
Approach: We propose single-shot multibox detector (SSD) networks with a side branch for 1-class detection without distinguishing the tooth type and for 16-class detection (i.e., the central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, and third molar, distinguished by the upper and lower jaws). In addition, post-processing was conducted to integrate the results of the two networks and categorize them into 32 classes, differentiating between the left and right teeth. The proposed method was applied to 950 dental panoramic radiographs obtained at multiple facilities, including a university hospital and dental clinics.
Results: The recognition performance of the SSD with a side branch was better than that of the original SSD. In addition, the detection rate was improved by the integration process. As a result, the detection rate was 99.03%, the number of false detections was 0.29 per image, and the classification rate was 96.79% for 32 tooth types.
Conclusions: We propose a method for tooth recognition using object detection and post-processing. The results show the effectiveness of network branching on the recognition performance and the usefulness of post-processing for neural network output.
The purpose of this study is to analyze dental panoramic radiographs for completing dental files to contribute to the diagnosis by dentists. In this study, we recognized 32 tooth types and classified four tooth attributes (tooth, remaining root, pontic, and implant) using 925 dental panoramic radiographs. YOLOv4 and post-processing were used for the recognition of 32 tooth types. As a result, the tooth detection recall was 99.65%, the number of false positives was 0.10 per image, and the 32-type recognition recall was 98.55%. For the classification of the four tooth attributes, two methods were compared. In Method 1, image classification was performed using a clipped image based on the tooth detection result. In Method 2, the labels of tooth attributes were added to the labels of tooth types in object detection. By providing two labels for the same bounding box, we performed multi-label object detection. The accuracy of Method 1 was 0.995 and that of Method 2 was 0.990. Method 2 uses a simple and robust model yet has comparable accuracy as Method 1. In addition, Method 2 did not require additional CNN models. This suggested the usefulness of multi-label detection.
The purpose of this study is to analyze dental panoramic radiographs for completing dental files to contribute to the diagnosis by dentists. As the initial stage, we detected each tooth and classified its tooth type. Since the final goal of this study includes multiple tasks, such as determination of dental conditions and recognition of lesions, we proposed a multitask training based on a Single Shot Multibox Detector (SSD) with a branch to predict the presence or absence of a tooth. The results showed that the proposed model improved the detection rate by 1.0%, the number of false positives per image by 0.03, and the detection rate by tooth type (total number of successfully detected and classified teeth/total number of teeth) by 1.6% compared with the original SSD, suggesting the effectiveness of the multi-task learning in dental panoramic radiographs. In addition, we integrated results of single-class detection without distinguishing the tooth type and 16-class (central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, third molar, distinguished by upper and lower jaws) detection for improving the detection rate and included post-processing for classification of teeth into 32 types and correction of tooth numbering. As a result, the detection rate of 98.8%, 0.33 false positives per image, and classification rate of 92.4% for 32 tooth types were archived.
Dental record plays an important role in dental diagnosis and personal identification. Automatic image preinterpretation can help reducing dentists’ workload and improving diagnostic efficiency. Systematic dental record filing enables effective utilization of accumulated records at dental clinics for forensic identification. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our previous method, two separate networks were employed for detection and classification of teeth. Although detection accuracy was promising, classification performance had a room of improvement. The purpose of this study was to investigate the use of the relation network to utilize information of positional relationship between teeth for the detection and classification. Using the proposed method, both detection and classification performance improved. Especially, the tooth type classification accuracy improved. The proposed method can be useful in automatic filing of the dental chart.
Tooth pulp atrophy occurs with increasing age. An age estimation procedure using dental cone beam computed tomography (CBCT) imaging was developed. Clinical dental CBCT images of 60 patients (aged from 20 to 80 years) were evaluated. The ratio of the cross-sectional area of the pulp cavity to the cross-sectional area of the tooth (pulp cavity ratio) was calculated. The pulp cavity ratio in the labio-lingual plane of the mandibular anterior teeth and the mesio-distal plane of the maxillary anterior teeth was strongly correlated with the patients’ age. The pulp cavity ratio of anterior teeth may be a useful parameter for estimating age.
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