Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS AND METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Engineering Degradation Rate of Polyphosphazene-Based Layer-by-Layer Polymer Coatings.

Degradable layer-by-layer (LbL) polymeric coatings have distinct advantages over traditional biomedical coatings due to their precision of assembly, versatile inclusion of bioactive molecules, and conformality to the complex architectures of implantable devices. However, controlling the degradation rate while achieving biocompatibility has remained a challenge. This work employs polyphosphazenes as promising candidates for film assembly due to their inherent biocompatibility, tunability of chemical composition, and the buffering capability of degradation products. The degradation of pyrrolidone-functionalized polyphosphazenes was monitored in solution, complexes and LbL coatings (with tannic acid), providing the first to our knowledge comparison of solution-state degradation to solid-state LbL degradation. In all cases, the rate of degradation accelerated in acidic conditions. Importantly, the tunability of the degradation rate of polyphosphazene-based LbL films was achieved by varying film assembly conditions. Specifically, by slightly increasing the ionization of tannic acid (near neutral pH), we introduce electrostatic "defects" to the hydrogen-bonded pairs that accelerate film degradation. Finally, we show that replacing the pyrrolidone side group with a carboxylic acid moiety greatly reduces the degradation rate of the LbL coatings. In practical applications, these coatings have the versatility to serve as biocompatible platforms for various biomedical applications and controlled release systems.

Virtually-Planned Orthognathic Surgery Achieves an Accurate Condylar Position.

PURPOSE: Accuracy in orthognathic surgery with virtual planning has been reported, but detailed analysis of accuracy according to anatomic location, including the mandibular condyle, is insufficient. The purpose of this study was to compare the virtual plan and surgical outcomes and analyze the degree and distribution of errors according to each anatomic location. PATIENTS AND METHODS: This retrospective cohort study evaluated skeletal class III patients, treated with bimaxillary surgery. The primary predictor was anatomic locations that consisted of right and left condyles, maxilla, and the distal segment of the mandible. Other variables were age and gender. The primary outcome was surgical accuracy, defined as mean 3-dimensional distance error, mean absolute error, and mean error along the horizontal, vertical, and anteroposterior axes between the virtual plan and surgical outcomes. Landmarks were compared using a computational method based on affine transformation with a 1-time landmark setting. The mean errors were visualized with multidimensional scattergrams. Bivariate and regression statistics were computed. RESULTS: This study included 52 patients, 26 men and 26 women, with a mean age of 21 years and 3 months. The mean 3D distance errors for condylar landmarks, maxillary landmarks, and landmarks on the distal segment of the mandible were 1.03, 1.25, and 2.24 mm, respectively. Condylar landmarks, maxillary landmarks, and the landmarks on the distal segment of the mandible were positioned at 0.49 mm inferior, 0.28 mm anterior, and 1.25 mm inferior, respectively. The landmark errors for the distal segment of the mandible exhibited a wider distribution than those for condylar and maxillary landmarks. CONCLUSIONS: Agreement between the planned and actual outcome aided by virtual surgical planning was highest for the condyles, followed by the maxilla, and the distal segment of the mandible. It is important to consider the tendency for surgical errors in each anatomic location during operations.