The Application of Deep Learning on CBCT in Dentistry

Abstract

Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user’s proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.

Automated 3D Landmarking of the Skull: A Novel Approach for Craniofacial Analysis

“…While this craniofacial bone mask may only encompass part of the cranium, this allows it to be applicable to CBCTs collected on devices with limited focal views. In the future we plan to extend this work to a full cranial mask and utilize the development of superior CT and CBCT scanners [44, 45] and deep learning for DICOM segmentation [46]. Ultimately, we envision that our work will pave the way for genetic association studies pertaining to cranial shape and high-resolution investigations into the genetic determinants influencing craniofacial bone morphology.…”

A novel approach to craniofacial analysis using automated 3D landmarking of the skull

“…Although this craniofacial bone mask may only cover a portion of the cranium, it facilitates applicability to CBCTs acquired from devices with limited focal views. In the future we plan to extend this work to a full cranial mask and utilize the development of superior CBCT scanners 46 , 47 and deep learning for DICOM segmentation 48 . Although the mask does provide over 6000 quasi-landmarks, it is limited by the less-accurate masking of the gonial area.…”

Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models

“…Of course, dentistry has not been unaffected by this intense process and AI has had a transformative impact on dental care delivery. It has improved diagnostic accuracy, enhanced treatment planning and working length determination, facilitated predictive analytics, detection and diagnosis of vertical root fractures, enhanced patient education, and expanded access to dental care through teledentistry [21][22][23][24][25][26][27][28][29][30]. As AI continues to advance, it holds the potential to further revolutionize dental care and improve patient outcomes.…”