Construction and Evaluation of an AI-based CBCT Resolution Optimization Technique for Extracted Teeth.

INTRODUCTION: In dental clinical practice, cone-beam computed tomography (CBCT) is commonly used to assist practitioners to recognize the complex morphology of root canal systems; however, because of its resolution limitations, certain small anatomical structures still cannot be accurately recognized on CBCT. The purpose of this study was to perform image super-resolution (SR) processing on CBCT images of extracted human teeth with the help of a deep learning model, and to compare the differences among CBCT, super-resolution computed tomography (SRCT), and micro-computed tomography (Micro-CT) images through three-dimensional reconstruction. METHODS: The deep learning model (Basicvsr++) was selected and modified. The dataset consisted of 171 extracted teeth that met inclusion criteria, with 40 maxillary first molars as the training set and 40 maxillary first molars as well as 91 teeth from other tooth positions as the external test set. The corresponding CBCT, SRCT, and Micro-CT images of each tooth in test sets were reconstructed using Mimics Research 17.0, and the root canal recognition rates in the 3 groups were recorded. The following parameters were measured: volume of hard tissue (V1), volume of pulp chamber and root canal system (V2), length of visible root canals under orifice (VL-X, where X represents the specific root canal), and intersection angle between coronal axis of canal and long axis of tooth (∠X, where X represents the specific root canal). Data were statistically analyzed between CBCT and SRCT images using paired sample t-test and Wilcoxon test analysis, with the measurement from Micro-CT images as the gold standard. RESULTS: Images from all tested teeth were successfully processed with the SR program. In 4-canal maxillary first molar, identification of MB2 was 72% (18/25) in CBCT group, 92% (23/25) in SRCT group, and 100% (25/25) in Micro-CT group. The difference of hard tissue volume between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT in all tested teeth except 4-canal mandibular first molar (P < .05). Similar results were obtained in volume of pulp chamber and root canal system in all tested teeth (P < .05). As for length of visible root canals under orifice, the difference between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT (P < .05) in most root canals. CONCLUSIONS: The deep learning model developed in this study helps to optimize the root canal morphology of extracted teeth in CBCT. And it may be helpful for the identification of MB2 in the maxillary first molar.

Quantifiable design and comparative evaluation of straight-line minimally invasive endodontic cavity based on the anatomical features of the coronal part of root canal.

Background/purpose: Minimally invasive endodontics has recently become popular in research. This study aimed to develop a new quantifiable straight-line minimally invasive endodontic cavity (SMIEC) for 3-rooted maxillary first molar based on the anatomical features of the coronal part of root canal. Materials and methods: Cone-beam computed tomography (CBCT) images of 80 teeth were converted into models in Mimics Research software. Anatomical features of the coronal part of root canal were measured to develop SMIECs with straight-line accesses to root canals in 3-matic Research software. Twenty models were randomly sampled and each was duplicated for 8 simulation groups: non-treated (NT), traditional endodontic cavity (TEC), ninja endodontic cavity (NEC) and 5 SMIECs. Post-simulation models were subjected to finite element analysis to detect von-Mises stresses in ABAQUS software. Results: Distributions of straight-line accesses to protogenetic root canals had certain manners, hence we developed 5 SMIECs. NEC and SMIECs had less hard tissue loss than TEC and presented different numerical rankings in different structures (P < 0.05). NEC had a less narrow surgery field than SMIECs except SMIEC2/4 (P < 0.05). The peak pericervical stresses of SMIECs were similar, lower than TEC and higher than NEC and NT (P < 0.05). The stress distributions of the 8 groups had certain manners. Conclusion: Five SMIECs with straight-line accesses to root canals were developed, whose biomechanical properties were worse than NEC but better than TEC. Having appropriate structure preservation and surgery field, SMIEC2/4 was a preferred SMIEC.