A novel CAD/CAM composite occlusal splint for intraoperative verification in single-splint two-jaw orthognathic surgery.

BACKGROUND: Previous computer-generated splints were designed and produced without modification than the traditional occlusal splints, which did not facilitate surgeon's intraoperative judgment in the single-splint two-jaw orthognathic surgery. Modifications of the digital occlusal splint can be achieved using computer-aided design and computer-aided manufacturing (CAD/CAM) software. This study reported the design, clinical application and validation of a novel CAD/CAM occlusal splint. METHODS: The maxillary and mandibular segments were fixed into the final occlusal splint and moved to the planned position according to the 3-dimensional simulation. The composite occlusal splint has 4 orthogonal bars to facilitate intraoperative assessment of the dental and skeletal midline, facial soft tissue midline, occlusal plane, upper tooth show, facial symmetry and facial bone position. To validate the surgical outcome, 5 parameters including pitch, roll and yaw rotations, midline deviation and chin position were measured on the virtual plan and the postoperative cone-beam computed tomography images to quantify the difference. RESULTS: The results showed no significant differences in the 5 parameters between the simulation and postoperative images. The root-mean-square difference between the conventional splints and CAD/CAM surgical splint ranged from 0.18 to 0.31 mm by superimposition of the two image models. All patients were satisfied with the treatment outcomes. Overall, this novel occlusal splint is ideal for verification of the maxillomandibular position during surgery. CONCLUSION: The novel composite occlusal splint provided useful and informative check to verify the maxillomandibular complex (MMC) position and facial appearance in single-splint two-jaw orthognathic surgery.

Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model: A Preliminary Experience.

PURPOSE: An objective and quantitative assessment of facial symmetry is essential for the surgical planning and evaluation of treatment outcomes in orthognathic surgery (OGS). This study applied the transfer learning model with a convolutional neural network based on 3-dimensional (3D) contour line features to evaluate the facial symmetry before and after OGS. METHODS: A total of 158 patients were recruited in a retrospective cohort study for the assessment and comparison of facial symmetry before and after OGS from January 2018 to March 2020. Three-dimensional facial photographs were captured by the 3dMD face system in a natural head position, with eyes looking forward, relaxed facial muscles, and habitual dental occlusion before and at least 6 months after surgery. Three-dimensional contour images were extracted from 3D facial images for the subsequent Web-based automatic assessment of facial symmetry by using the transfer learning with a convolutional neural network model. RESULTS: The mean score of postoperative facial symmetry showed significant improvements from 2.74 to 3.52, and the improvement degree of facial symmetry (in percentage) after surgery was 21% using the constructed machine learning model. A Web-based system provided a user-friendly interface and quick assessment results for clinicians and was an effective doctor-patient communication tool. CONCLUSIONS: This work was the first attempt to automatically assess the facial symmetry before and after surgery in an objective and quantitative value by using a machine learning model based on the 3D contour feature map.

Fat Grafting in Patients With Extensive Unilateral Facial Deficiency: Three-Dimensional Computer-Assisted Planning, Implementation, and Outcome Assessment.

PURPOSE: Autologous fat injection is a widely used, simple, and less invasive technique to correct volume deficiency. This study developed a treatment method by using a 3-dimensional (3D) simulation to plan and implement fat injection in patients with an extensive facial deficiency and then validated the accuracy of the method and treatment outcomes. METHODS: Seven patients with a large unilateral facial deficiency receiving autologous fat grafts between 2015 and 2017 were recruited. One patient received repeated treatment. Furthermore, 3D surgical simulation was used to measure the difference between the mirrored image and lesion side. An extra 20% to 30% of fat graft was added. A color map was provided, and contour lines 2 mm deep marked the location of the fat injection. Outcome assessments were then performed, and a 3D symmetry index was defined using the contour lines on the facial surface. RESULTS: No significant difference was noted between the predicted volume and postoperative fat graft retention (35.7 ± 7.4 and 31.6 ± 9.7 mL, respectively; P = 0.176). A comparison of preoperative (79.5% ± 4.3%) and postoperative (89.0% ± 3.3%) 3D symmetry indexes indicated significantly improved facial symmetry (P = 0.018). Patient-reported outcomes of satisfaction on FACE-Q questionnaires yielded an average score of 62.73, higher than the control score (59.83). CONCLUSIONS: By using the proposed method, we could predict the required fat graft volume; moreover, the contoured map aided accurate surgical implementation. Thus, this method is useful for planning and guiding fat grafting treatment in patients with major unilateral facial deficiency.