This study was performed to investigate the incidence and configuration of the bifid mandibular canal in a Korean population by using cone-beam computed tomography (CBCT) imaging. CBCT images of 1933 patients (884 male and 1049 female) were evaluated using PSR-9000N and Alphard-Vega 3030 Dental CT units (Asahi Roentgen Ind. Co., Ltd, Kyoto, Japan). Image analysis was performed by using OnDemand3D software (CyberMed Inc., Seoul, Korea). The bifid mandibular canal was identified and classified into four types, namely, the forward canal, buccolingual canal, dental canal, and retromolar canal. Statistical analysis was performed by using the chi-squared test and one-way analysis of variance (ANOVA). Bifid mandibular canals were observed in 198 (10.2%) of 1933 patients. The most frequently observed type of bifid mandibular canal was the retromolar canal (n=104, rate: 52.5%) without any significant difference among the incidence of each age and gender. The mean diameter of the accessory canal was 1.27 mm (range: 0.27-3.29 mm) without any significant difference among the mean diameter of each type of the bifid mandibular canal. The mean length of the bifid mandibular canals was 14.97mm(range: 2.17-38.8 mm) with only a significant difference between the dental canal and the other types. The bifid mandibular canal is not uncommon in Koreans and has a prevalence of 10.2% as indicated in the present study. It is suggested that a CBCT examination be recommended for detecting a bifid canal.

This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preprocessing phase. The intra-examiner reliability was high (ICC = 0.99) in manual identification. The results of prediction of the system for average mean radial error (MRE) and standard deviation (SD) were 1.03 mm and 1.29 mm, respectively. In conclusion, different types of image data might be the one of factors that affect the prediction accuracy of a fully-automated landmark identification system, based on multi-stage CNNs.

The aim of this study was to present an optimal diagnostic protocol by comparing and analyzing a conventional examination and the quantitative light-induced fluorescence (QLF) technique. Selected were 297 teeth of 153 patients to take QLF images and bitewing radiographs. Occlusal dental caries, proximal dental caries and cracks were evaluated and scored using QLF, X-ray and/or visual criteria. The sensitivity, specificity, and area under the curve (AUC) of a receiver operating characteristic analysis were calculated. Two fluorescence parameters (|ΔFmax| and ΔRmax) were utilized to evaluate the fluorescence pattern according to the severity of lesions based on QLF or X-ray criteria. QLF showed higher scores for detecting occlusal dental caries and cracks than the conventional method. ΔRmax increased more clearly than ΔFmax did with occlusal dental caries. The |ΔFmax| values of occlusal dental caries, proximal dental caries and cracks showed good AUC levels (0.84, 0.81 and 0.83, respectively). The ΔRmax of occlusal dental caries showed the highest AUC (0.91) and the ΔRmax of proximal dental caries showed a fail level (0.59) compared to bitewing radiographs. The QLF image could visualize and estimate the degree of occlusal dental caries or cracks. Consequently, the QLF technique may be an adjunct tool to conventional methods for the detection of occlusal caries and peripheral cracks.

Ahn BS, et al. Imaging Sci Dent. 2020 Jun;50(2):125-132. https://doi.org/10.5624/isd.2020.50.2.125

Park CW, et al. Imaging Sci Dent. 2017 Sep;47(3):165-174. https://doi.org/10.5624/isd.2017.47.3.165