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.
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