Design and optimization of a novel patient-specific subperiosteal implant additively manufactured in yttria-stabilized zirconia.

OBJECTIVE: To design a patient-specific subperiosteal implant for a severely atrophic maxillary ridge using yttria-stabilized additively manufactured zirconia (3YSZ) and evaluate its material properties by applying topology optimization (TO) to replace bulk material with a lattice structure. MATERIALS: A contrast-based segmented skull model from anonymized computed tomography data of a patient was used for the initial anatomical design of the implant for the atrophic maxillary ridge. The implant underwent finite element analysis (FEA) and TO under different occlusal load-bearing conditions. The resulting implant designs, in bulk material and lattice, were evaluated via in-silico tensile tests and 3D printed. RESULTS: The workflow produced two patient-specific subperiosteal designs: a) an anatomically precise bulk implant, b) a TO lattice implant. In-silico tensile tests revealed that the Young's modulus of yttria-stabilized zirconia is 205 GPa for the bulk material and 83.3 GPa for the lattice. Maximum principal stresses in the implant were 61.14 MPa in bulk material and 278.63 MPa in lattice, both tolerable, indicating the redesigned implant can withstand occlusal forces of 125-250 N per abutment. Furthermore, TO achieved a 13.10 % mass reduction and 208.71 % increased surface area, suggesting improved osteointegration potential. SIGNIFICANCE: The study demonstrates the planning and optimization of ceramic implant topology. A further iteration of the implant was successfully implanted in a patient-named use case, employing the same fabrication process and parameters.

Clinical performance of additively manufactured subperiosteal implants: a systematic review.

PURPOSE: The aim of this study was to assess implant survival and complications rate of modern subperiosteal implants (CAD designed and additively manufactured). METHODS: A systematic review was conducted using three electronic databases; Medline (Pubmed), Cochrane library, and SCOPUS, following the PRISMA statement recommendations to answer the PICO question: "In patients with bone atrophy (P), do additively manufactured subperiosteal implants (I), compared to subperiosteal implants manufactured following traditional approaches (c), present satisfactory implant survival and complication rates (O)? The study was pre-registered in PROSPERO (CRD42023424211). Included articles quality was assessed using the "NIH quality assessment tools". RESULTS: Thirteen articles were finally selected (5 cohort studies and 8 case series), including 227 patients (121 female / 106 male; weighted mean age 62.4 years) and 227 implants. After a weighted mean follow-up time of 21.4 months, 97.8% of implants were in function (5 failures reported), 58 implants (25.6%) presented partial exposure, 12 patients (5.3%) suffered soft tissue or persistent infection. Fracture of the interim prosthesis was reported in 8 of the155 patients (5.2%) in which the use of a provisional prosthesis was reported. A great heterogeneity was found in terms of study design and methodological aspects. For this reason, a quantitative analysis followed by meta-analysis was not possible. CONCLUSIONS: Within the limitations of this study, modern additively manufactured subperiosteal implants presented a good survival in the short-time, but a noticeable number of soft-tissue related complications were reported. Further studies are needed to assess the clinical behavior in the medium- and long-term.

Machine learning based prediction of perioperative blood loss in orthognathic surgery.

The aim of this study was to evaluate, if and with what accuracy perioperative blood loss can be calculated by a machine learning algorithm prior to orthognathic surgery. The investigators implemented a random forest algorithm to predict perioperative blood loss. 1472 patients who underwent orthognathic surgery from 01/2006 to 06/2017 at our institution were screened and 950 patients were included and separated 80%/20% in a training set - utilized to generate the prediction model - and a testing set - utilized to estimate the accuracy of the model. The outcome variable was the correlation between actual perioperative blood loss and predicted perioperative blood loss in the testing set. Other study variables were the difference of actual and predicted perioperative blood loss and important factors influencing perioperative blood loss using random forest feature importance. Descriptive and bivariate statistics were computed and the P value was set at 0.05. There was a statistically significant correlation between actual perioperative blood loss and predicted perioperative blood loss (p < 0.001). The mean difference was 7.4 ml with a standard deviation of 172.3 ml. The results of this study suggest that the application of a machine-learning algorithm allows a prediction of perioperative blood loss prior to orthognathic surgery.