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Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists experiences due to the abnormality and large-scale variance of patients teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooths region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.964±0.054 , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of 0.597±0.761 mm in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.

We hypothesized that the different post-implantation healing stages between the conventional and immediate implantations produce different amounts and tissue composition of the peri-implant bone. Thus, the objective of the current study was to examine whether the stability of dental implant systems is associated with characteristics of the interfacial bone area at different post-implanation healing periods. Mandibular molars were extracted from each beagle dog. After 10 weeks post-extraction, a screw-type titanium dental implant was placed in the molar location following a conventional dental implant (Con) procedure. Simultaneously, mandibular premolars were extracted and the same type of dental implant was placed in the distal site of the extracted premolar root following an immediate dental implant (Imm) procedure. The implant stability quotient (ISQ) values were not significantly different between Con and Imm groups at 0-, 3-, and 6-weeks post-implantation. However, 3D micro-computed tomography and 2D histological images confirmed that the Imm system had more gaps between the bone and implant than the Con system. On the other hand, the nanoindentation modulus value at the bone–implant interface was significantly higher for the Imm group than the Con group at both 3 weeks and 6 weeks post-implantation. The current results from multi-scale characterization suggest that the higher interfacial bone quality of the Imm system, despite its earlier post-implantation stage, plays a crucial role in maintaining stability comparable to that of the Con system. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice. [ABSTRACT FROM AUTHOR]

The aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) is internationally recognized as a reliable and valid method for assessing aesthetic treatment need. The objective of this study is to use artificial intelligence (AI) to automate the AC assessment. A total of 1009 pre-treatment frontal intraoral photos with overjet values were collected. Each photo was graded by an experienced calibration clinician. The AI was trained using the intraoral images, overjet, and two other approaches. For Scheme 1, the training data were AC 1–10. For Scheme 2, the training data were either the two groups AC 1–5 and AC 6–10 or the three groups AC 1–4, AC 5–7, and AC 8–10. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were measured for all approaches. The performance was tested without overjet values as input. The intra-rater reliability for the grader, using kappa, was 0.84 (95% CI 0.76–0.93). Scheme 1 had 77% sensitivity, 88% specificity, 82% accuracy, 89% PPV, and 75% NPV in predicting the binary groups. All other schemes offered poor tradeoffs. Findings after omitting overjet and dataset supplementation results were mixed, depending upon perspective. We have developed deep learning-based algorithms that can predict treatment need based on IOTN-AC reference standards; this provides an adjunct to clinical assessment of dental aesthetics. [ABSTRACT FROM AUTHOR]

Polydopamine, a mussel-inspired self-adherent polymer of dopamine, has impressive adhesive properties and thus is one of the most versatile approaches to functionalize tissue engineering scaffolds. To date, many types of polydopamine-functionalized scaffolds have been manufactured and extensively applied in bone tissue engineering at the preclinical stage. However, how polydopamine is biodegraded and metabolized during the bone healing process and the side effects of its metabolite remain largely unknown. These issues are often neglected in the modern manufacture of polydopamine-functionalized materials and restrict them from stepping forward to clinical applications. In this study, using our bioinspired polydopamine-laced hydroxyapatite collagen calcium silicate material as a representative of polydopamine-functionalized tissue engineering scaffolds, we discovered that polydopamine can be metabolized to dopamine specifically by osteoclasts, which we termed "osteoclast-driven polydopamine-to-dopamine release". The released dopamine showed an osteoinductive effect in vitro and promoted bone regeneration in calvarial critical-sized defects. The concept of "osteoclast-driven polydopamine-to-dopamine release" has considerable application potential. It could be easily adopted by other existing polydopamine-functionalized scaffolds: just by recruiting osteoclasts. Once adopted, scaffolds will obtain a dopamine-releasing function, which enables their modulation of osteoblast activity and hence elevates the osteoinductive effect. Thus, "osteoclast-driven polydopamine-to-dopamine release" serves as an upgrade patch, which is useful for many existing polydopamine-functionalized materials. [ABSTRACT FROM AUTHOR]