Application of three-dimensional reconstruction technology in dentistry: a narrative review.

BACKGROUND: Three-dimensional(3D) reconstruction technology is a method of transforming real goals into mathematical models consistent with computer logic expressions and has been widely used in dentistry, but the lack of review and summary leads to confusion and misinterpretation of information. The purpose of this review is to provide the first comprehensive link and scientific analysis of 3D reconstruction technology and dentistry to bridge the information bias between these two disciplines. METHODS: The IEEE Xplore and PubMed databases were used for rigorous searches based on specific inclusion and exclusion criteria, supplemented by Google Academic as a complementary tool to retrieve all literature up to February 2023. We conducted a narrative review focusing on the empirical findings of the application of 3D reconstruction technology to dentistry. RESULTS: We classify the technologies applied to dentistry according to their principles and summarize the different characteristics of each category, as well as the different application scenarios determined by these characteristics of each technique. In addition, we indicate their development prospects and worthy research directions in the field of dentistry, from individual techniques to the overall discipline of 3D reconstruction technology, respectively. CONCLUSIONS: Researchers and clinicians should make different decisions on the choice of 3D reconstruction technology based on different objectives. The main trend in the future development of 3D reconstruction technology is the joint application of technology.

Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms.

OBJECTIVES: The aim of this study was to develop an assessment tool for automatic detection of dental caries in periapical radiographs using convolutional neural network (CNN) architecture. METHODS: A novel diagnostic model named ResNet + SAM was established using numerous periapical radiographs (4278 images) annotated by medical experts to automatically detect dental caries. The performance of the model was compared to the traditional CNNs (VGG19, ResNet-50), and the dentists. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique shows the region of interest in the image for the CNNs. RESULTS: ResNet + SAM demonstrated significantly improved performance compared to the modified ResNet-50 model, with an average F1 score of 0.886 (95% CI 0.855-0.918), accuracy of 0.885 (95% CI 0.862-0.901) and AUC of 0.954 (95% CI 0.924-0.980). The comparison between the performance of the model and the dentists revealed that the model achieved higher accuracy than that of the junior dentists. With the assist of the tool, the dentists achieved superior metrics with a mean F1 score of 0.827 and the interobserver agreement for dental caries is enhanced from 0.592/0.610 to 0.706/0.723. CONCLUSIONS: According to the results obtained from the experiments, the automatic assessment tool using the ResNet + SAM model shows remarkable performance and has excellent possibilities in identifying dental caries. The use of the assessment tool in clinical practice can be of great benefit as a clinical decision-making support in dentistry and reduce the workload of dentists.

Accuracy of RGB-D camera-based and stereophotogrammetric facial scanners: a comparative study.

OBJECTIVES: This study aimed to evaluate and compare the accuracy and inter-operator reliability of a low-cost red-green-blue-depth (RGB-D) camera-based facial scanner (Bellus3D Arc7) with a stereophotogrammetry facial scanner (3dMD) and to explore the possibility of the former as a clinical substitute for the latter. METHODS: A mannequin head was selected as the research object. In the RGB-D camera-based facial scanner group, the head was continuously scanned five times using an RGB-D camera-based facial scanner (Bellus3D Arc7), and the outcome data of each scan was then imported into CAD software (MeshLab) to reconstruct three-dimensional (3D) facial photographs. In the stereophotogrammetry facial scanner group, the mannequin head was scanned with a stereophotogrammetry facial scanner (3dMD). Selected parameters were directly measured on the reconstructed 3D virtual faces using a CAD software. The same parameters were then measured directly on the mannequin head using the direct anthropometry (DA) method as the gold standard for later comparison. The accuracy of the facial scanners was evaluated in terms of trueness and precision. Trueness was evaluated by comparing the measurement results of the two groups with each other and with that of DA using equivalence tests and average absolute deviations, while precision and inter-operator reliability were assessed using the intraclass correlation coefficient (ICC). A 3D facial mesh deviation between the two groups was also calculated for further reference using a 3D metrology software (GOM inspect pro). RESULTS: In terms of trueness, the average absolute deviations between RGB-D camera-based and stereophotogrammetry facial scanners, between RGB-D camera-based facial scanner and DA, and between stereophotogrammetry facial scanner and DA were statistically equivalent at 0.50±0.27 mm, 0.61±0.42 mm, and 0.28±0.14 mm, respectively. Equivalence test results confirmed that their equivalence was within clinical requirements (<1 mm). The ICC for each parameter was approximately 0.999 in terms of precision and inter-operator reliability. A 3D facial mesh analysis suggested that the deviation between the two groups was 0.37±0.01 mm. CONCLUSIONS: For facial scanners, an accuracy of <1 mm is commonly considered clinically acceptable. Both the RGB-D camera-based and stereophotogrammetry facial scanners in this study showed acceptable trueness, high precision, and inter-operator reliability. A low-cost RGB-D camera-based facial scanner could be an eligible clinical substitute for traditional stereophotogrammetry. CLINICAL SIGNIFICANCE: The low-cost RGB-D camera-based facial scanner showed clinically acceptable trueness, high precision, and inter-operator reliability; thus, it could be an eligible clinical substitute for traditional stereophotogrammetry.