In this study, the practical accuracy (PA) of optical facial scanners for facial deformity patients in oral clinic was evaluated. Ten patients with a variety of facial deformities from oral clinical were included in the study. For each patient, a three-dimensional (3D) face model was acquired, via a high-accuracy industrial “line-laser” scanner (Faro), as the reference model and two test models were obtained, via a “stereophotography” (3dMD) and a “structured light” facial scanner (FaceScan) separately. Registration based on the iterative closest point (ICP) algorithm was executed to overlap the test models to reference models, and “3D error” as a new measurement indicator calculated by reverse engineering software (Geomagic Studio) was used to evaluate the 3D global and partial (upper, middle, and lower parts of face) PA of each facial scanner. The respective 3D accuracy of stereophotography and structured light facial scanners obtained for facial deformities was 0.58±0.11 mm and 0.57±0.07 mm. The 3D accuracy of different facial partitions was inconsistent; the middle face had the best performance. Although the PA of two facial scanners was lower than their nominal accuracy (NA), they all met the requirement for oral clinic use.

Background: Cone-beam computed tomography (CBCT) is the most widely used method for postsurgical evaluation of the accuracy of guided implant surgery. However, the disadvantages of CBCT include radiation exposure, artifacts caused by metal implants, and high cost. Few studies have introduced a digital registration method to replace CBCT for evaluating the accuracy of guided surgery. The purpose of this study was to compare digital registration to conventional CBCT in terms of the capacity to evaluate the implant positioning accuracy of guided surgery. Materials and methods: This in vitro study included 40 acrylic resin models with posterior single mandibular tooth loss. Guided surgery software was used to determine the optimal implant position; 40 tooth-supported fully guided drilling templates were designed and milled accordingly. After the guided surgery, the accuracies of the surgical templates were evaluated by conventional CBCT and digital registration. For evaluation by conventional CBCT, postsurgical CBCT scans of the resin models were performed. The CBCT data were reconstructed and superimposed on the implant planning data. For digital registration, we constructed a virtual registration unit that consisted of an implant replica and a scan body. Next, we obtained postsurgical optical scans of resin models with the scan body. The postsurgical implant position was identified by superimposition of the registration unit and optical scan data. The implant planning data and postsurgical implant position data were superimposed; deviations were reported in terms of distance for implant entry/apex point and in terms of angle for the implant axis. Interclass correlation coefficients (ICCs) and Bland–Altman plots were used to analyze the agreement between the two evaluation methods. Results: The ICCs between the two methods were 0.986, 0.993, and 0.968 for the entry point, apex point, and angle, respectively; all were significantly greater than 0.75 (p < 0.001). Bland–Altman plots showed that the 95% limits of agreement of the differences were − 0.144 to + 0.081 mm, − 0.135 to + 0.147 mm, and − 0.451° to + 0.729° for the entry point, apex point, and angle, respectively; all values were within the maximum tolerated difference. Conclusion: Conventional CBCT and digital registration showed good agreement in terms of evaluating the accuracy of implant positioning using tooth-supported surgical templates.

Background: We aimed to establish a novel method, using the weighted Procrustes analysis (WPA) algorithm, which assigns weight to facial anatomical landmarks, to construct a three-dimensional facial symmetry reference plane (SRP) for mandibular deviation patients. Methods: Three-dimensional facial SRPs were independently extracted from 15 mandibular deviation patients using both our WPA algorithm and the standard PA algorithm. A reference plane was defined to serve as the ground truth. To determine whether the WPA SRP or the PA SRP was closer to the ground truth, we measured the position error of mirrored landmarks, the facial asymmetry index (FAI) error, and the angle error for the global face and each facial third partition. Results: The average angle error between the WPA SRP and the ground truth was 1.66 ± 0.81°, which was smaller than that between the PA SRP and the ground truth. The position error of the mirrored landmarks constructed using the WPA algorithm in the global face (3.64 ± 1.53 mm) and each facial partition was lower than that constructed using the PA algorithm. The average FAI error of the WPA SRP was − 7.77 ± 17.02 mm, which was smaller than that of the PA SRP. Conclusions: This novel automatic algorithm, based on weighted anatomic landmarks, can provide a more adaptable SRP than the standard PA algorithm when applied to severe mandibular deviation patients and can better simulate the diagnosis strategies of clinical experts.

Background: The aim of this study was to assess the difference of palatal morphology in different vertical patterns between skeletal Class I subjects and skeletal Class II subjects with retrusive mandible. Methods: Seventy-six skeletal Class II subjects with retrusive mandible (38 females, 38 males) and 85 skeletal Class I subjects (45 females, 40 males) were collected retrospectively and divided into hyperdivergent, normodivergent and hypodivergent groups. CBCT images of these subjects were reoriented by Dolphin 3D Imaging software. Three-dimensional (3D) maxilla was segmented by ProPlan software before using Geomagic Studio software to reconstruct 3D palatal morphology. Deviation patterns on 3D colored map analysis was performed to compare the difference of 3D palatal morphology between different groups. Results: 3D colored map analysis showed that male’s palate was higher and wider than that of female in the posterior part, regardless of different sagittal and vertical patterns. In skeletal Class II subjects with retrusive mandible, males with hyperdivergent and normodivergent showed higher and narrower in the posterior part of palate, while females with hyperdivergent and normodivergent had a higher but no obviously narrow palate compared with the hypodivergent subjects. Skeletal Class II subjects with retrusive mandible showed flatter and narrower in the posterior part of palate than that of skeletal Class I subjects. Conclusions: Sagittal and vertical patterns have great influence on the palatal morphology and as the vertical dimension increased, the palate tended to be higher and narrower.

Three-dimensional symmetry and coordination are important factors in facial aesthetics, and analysis of facial asymmetry is the basis for clinical diagnosis, treatment, and doctor–patient communication. With the development of three-dimensional measurement and data analysis technology, facial asymmetry analysis methods are mainly based on facial anatomic landmarks, original-mirror alignment algorithm, facial anthropometric mask, and artificial intelligence. This review summarizes the methods of three-dimensional facial asymmetry analysis, and current research progress in the field. The advantages and limitations of various methods are analyzed and discussed to provide a reference for oral clinical application.

(1) Background: Three-dimensional (3D) facial anatomical landmarks are the premise and foundation of facial morphology analysis. At present, there is no ideal automatic determination method for 3D facial anatomical landmarks. This research aims to realize the automatic determination of 3D facial anatomical landmarks based on the non-rigid registration algorithm developed by our research team and to evaluate its landmark localization accuracy. (2) Methods: A 3D facial scanner, Face Scan, was used to collect 3D facial data of 20 adult males without significant facial deformities. Using the radial basis function optimized non-rigid registration algorithm, TH-OCR, developed by our research team (experimental group: TH group) and the non-rigid registration algorithm, MeshMonk (control group: MM group), a 3D face template constructed in our previous research was deformed and registered to each participant’s data. The automatic determination of 3D facial anatomical landmarks was realized according to the index of 32 facial anatomical landmarks determined on the 3D face template. Considering these 32 facial anatomical landmarks manually selected by experts on the 3D facial data as the gold standard, the distance between the automatically determined and the corresponding manually selected facial anatomical landmarks was calculated as the “landmark localization error” to evaluate the effect and feasibility of the automatic determination method (template method). (3) Results: The mean landmark localization error of all facial anatomical landmarks in the TH and MM groups was 2.34 ± 1.76 mm and 2.16 ± 1.97 mm, respectively. The automatic determination of the anatomical landmarks in the middle face was better than that in the upper and lower face in both groups. Further, the automatic determination of anatomical landmarks in the center of the face was better than in the marginal part. (4) Conclusions: In this study, the automatic determination of 3D facial anatomical landmarks was realized based on non-rigid registration algorithms. There is no significant difference in the automatic landmark localization accuracy between the TH-OCR algorithm and the MeshMonk algorithm, and both can meet the needs of oral clinical applications to a certain extent.