Artificial intelligence‐assisted determination of available sites for palatal orthodontic mini implants based on palatal thickness through CBCT

Abstract

Objectives To develop an artificial intelligence (AI) system for automatic palate segmentation through CBCT, and to determine the personalized available sites for palatal mini implants by measuring palatal bone and soft tissue thickness according to the AI‐predicted results. Materials and Methods Eight thousand four hundred target slices (from 70 CBCT scans) from orthodontic patients were collected, labelled by well‐trained orthodontists and randomly divided into two groups: a training set and a test set. After the deep learning process, we evaluated the performance of our deep learning model with the mean Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), sensitivity (SEN), positive predictive value (PPV) and mean thickness percentage error (MTPE). The pixel traversal method was proposed to measure the thickness of palatal bone and soft tissue, and to predict available sites for palatal orthodontic mini implants. Then, an example of available sites for palatal mini implants from the test set was mapped. Results The average DSC, ASSD, SEN, PPV and MTPE for the segmented palatal bone tissue were 0.831%, 1.122%, 0.876%, 0.815% and 6.70%, while that for the palatal soft tissue were 0.741%, 1.091%, 0.861%, 0.695% and 12.2%, respectively. Besides, an example of available sites for palatal mini implants was mapped according to predefined criteria. Conclusions Our AI system showed high accuracy for palatal segmentation and thickness measurement, which is helpful for the determination of available sites and the design of a surgical guide for palatal orthodontic mini implants.

Comparison between basal arch width in normal occlusions and class Ⅱ malocclusions by using cone-beam computed tomography

“…For asymmetric dentition, we need to find simple and stable landmarks and method independent of teeth to measure the width of the dental arch. In recent years, artificial intelligence based on machine learning with big data has gradually developed in the clinical and scientific research field of orthodontics and has been widely used in the measurement of oral and maxillofacial soft and hard tissues, the formulation of treatment plans and the prediction of treatment effects, etc., achieving credible results and high accuracy [ [30] , [31] , [32] , [33] ]. In future research, we will essay to use artificial intelligence technology based on deep learning to collect and measure 3D data of jaws, to obtain more comprehensive and real results.…”

Should Cone-Beam Computed Tomography Be Performed Prior to Orthodontic Miniscrew Placement in the Infrazygomatic Crest Area?—A Systematic Review

“…An interesting issue is the use of artificial intelligence-supported determination of available sites for orthodontic miniscrews based on bone analysis using the CBCT [ 56 , 57 ]. Today, it is not common in dentistry and orthodontics.…”

“…Today, it is not common in dentistry and orthodontics. The AI system demonstrated high accuracy in bone segmentation and measurement, which is helpful in identifying available sites and designing a surgical template for palatal TADs [ 56 ]. The use of AI for IZC miniscrew placement planning should be researched in the future.…”

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

“…During the orthodontic treatment, orthodontists often come across various challenges, including clinical expertise in orthodontics and patient communication and management. The application of AI can help facilitate efficient and effective orthodontic treatment regarding practice guidance, remote care and clinical documentation [152][153][154][155][156][157][158][159][160].…”

“…The 3D U-Net, a deep learning algorithm, is widely used in 3D image segmentation. As a modified version of 3D U-Net, 3D U-Net with squeeze-and-excitation modules (3D-UnetSE) has achieved better performance in capturing high level features [153,162]. The stability of palatal mini implants is associated with hard and soft tissues [163,164].…”