Model-Based Orthodontic Assessments for Dental Panoramic Radiographs

Abstract

For better treatment outcomes, dentists usually use a set of parameters for orthodontic evaluation. In this study, a new method is proposed to assist dentists in obtaining reliable assessment of these parameters. The proposed method is based on dental panoramic radiographs and can be divided into four stages: image preprocessing, model training, tooth segmentation, and assessment of orthodontic parameters. The image is first normalized and enhanced. Then, the model training stage consists of shape and image model training, energy function training, and weight training. Next, we automatically segment the tooth contours in an energy-minimized manner. Finally, the automatic assessment of orthodontic parameters is carried out. The experimental results show that the average of absolute distance, the Dice similarity coefficient, and the average qualitative score ranged between 4.17 and 6.03, 0.87 and 0.90, as well as 2.58 and 3.12, respectively. The orthodontic assessment also is close to the evaluation of orthodontists. It has been shown that the proposed method can obtain accurate and consistent measurement in helping dentists to obtain an objective treatment evaluation.

Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs

“…In the study of identifying tooth position, there is also a study by H. Chen et al [8] using Fast-RCNN and three post-processing methods to automatically distinguish the area where the tooth is located and label the tooth position. are also conducting similar research on DPR identification or segmentation, which not only shows that DPR has gradually become the main in dicator for judging dental conditions, it also shows automatic identification of DPR has become the current major trend [9], [10], [11], [12], [13]. The automatic image recognition of DPR has not only become the current major trend, but also reduces the time for manual inspection of DPR images.…”

A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

“…There are also approaches that explore the usage of optical coherence tomography for dental diagnosis, which can provide tooth status information in an noncontact and noninvasive manner [57]. Another approach estimates dental parameters directly, albeit for orthodontic assessment [58]. Still, this highlights the trend and need for automation of manual measurements and estimations in dentistry and dentistryrelated fields.…”

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

“…In dentistry, AI has been applied recently to detect carious lesions on bitewing radiographs using pre-trained convolutional neural networks (CNNs) [11]. It has also been used to identify periodontal bone loss (PBL) in peri-apical radiographs and the detection of apical lesions and treatment planning in panoramic dental radiographs [12][13][14][15][16]. Furthermore, given the complexity and the associated diagnostic efforts, the application of CNNs on panoramic radiographs may result in a higher diagnostic yield in terms of increased accuracy, reliability, and time savings for the assessment [12].…”