Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

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Purpose: Error testing at each stage of prosthetic manufacturing remains relatively underdeveloped for computer-aided design/computer-aided manufacturing methods, and no experimental studies have validated the computer-aided design programs. This study aimed to test the accuracy and trueness of the computer-aided design of a threeunit fixed prosthesis. Materials and Methods: Three computer-aided design programs (Exocad, Dental System TM , and inLab 16) were tested on the designs of a three-unit fixed partial denture, and a three-dimensional analysis program was used to calculate the internal clearance error for the computer-aided design prostheses. The Kruskal-Wallis and Dunn's post hoc tests were used to reveal significant differences in trueness between the three computer-aided design programs (α < 0.05). Results: Dental System TM showed the lowest mean error values for #24 and #26 at the mesial margin (both 0 µm), mesial wall (0.10, 0.12 µm, respectively), occlusal surface (-0.05, 0.10 µm), distal wall (0.23, -0.02 µm), and distal margin (both 0 µm). In sum, except for the mesial margin and distal margin site of tooth #26, the mean error value of Dental System TM was statistically the lowest, followed by those of Exocad and inLab 16 (p < 0.003). Conclusions:The accuracy of computer-aided design differed according to the type of computer-aided design program. Dental System TM achieved the best trueness at the margins, axial walls, and occlusal surface, followed by Exocad and inLab 16.

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Gradual tooth wear is a natural process of aging, but pathological wear over physiologic ranges leads to functional and esthetic problems. The loss of posterior support may cause pathological wear of anterior teeth, which results in reduction of vertical dimension and disharmony of occlusal plane. To solve this problem, determination of proper vertical dimension considering centric relation is necessary. This case report presented 71-year-old male, who had the severe wear of lower anterior teeth due to loss of posterior support. By meticulous evaluation, a full mouth rehabilitation with elevation of vertical dimension was planned. After 8 months of follow-up, stable occlusal scheme is maintained and patient was satisfied with clinical outcome functionally and esthetically. (J Korean Acad Prosthodont 2020;58:153-60)