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Background The role of injectable platelet rich fibrin (i-PRF) in orthodontic treatment has not been investigated with focus on its effect on dental and bony periodontal elements. Objective To evaluate the efficacy of i-PRF in bone preservation and prevention of root resorption. Methods A randomized split-mouth controlled trial included 21 patients aged 16–28 years (20.85 ± 3.85), who were treated for Class II malocclusion with the extraction of the maxillary first premolars. Right and left sides were randomly allocated to either experimental treated with i-PRF or control sides. After the leveling and alignment phase, the canines were retracted with 150gm forces. The i-PRF was prepared from the blood of each patient following a precise protocol, then injected immediately before canine retraction on the buccal and palatal aspects of the extraction sites. Localized maxillary cone beam computed tomography scans were taken before and after canine retraction to measure alveolar bone height and thickness and canine root length (indicative of root resorption), and the presence of dehiscence and fenestration. Paired sample t-tests and Wilcoxon signed rank tests were used to compare the changes between groups. Results No statistically significant differences in bone height, bone thickness were found between sides and between pre- and post-retraction period. However, root length was reduced post retraction but did not differ between sides. In both groups, postoperative dehiscence was observed buccally and palatally and fenestrations were recorded on only the buccal aspect. Conclusions I-PRF did not affect bone quality during canine retraction or prevent canine root resorption. I-PRF did not reduce the prevalence of dehiscence and fenestration. Trial registration ClinicalTrials.gov (identifier number: NCT 03399760. 16/01/2018).

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Objective: To evaluate stresses on maxillary teeth during alignment of a palatally impacted canine (PIC) under different loading conditions with forces applied in vertical and buccal directions. Materials and Methods: A three-dimensional finite element model of the maxilla was developed from a cone beam computed tomographic scan of a patient with a left PIC. Traction was simulated under different setups: (1) palatal spring extending from a transpalatal bar (TPB) anchored on the first molars (M1) and alternatively combined with different archwires (0.016 × 0.022-inch; 0.018 × 0.025-inch) with and without engaging second molars and (2) a buccal force against 0.018-inch, 0.016 × 0.022-inch, and 0.018 × 0.025-inch archwires with and without engaging the left lateral incisor (I2). Results: Without fixed appliances, stresses were assumed by M1; with fixed appliances, stresses were distributed on all teeth, decreasing mesially toward the midline. Direct buccal pull exerted most stress on neighboring I2 (19–20% with different wire sizes) and first premolar (12–17%), decreasing distally, along a similar pattern with different archwire sizes. When I2 was bypassed, stresses on adjacent teeth increased only by 3–6%. Higher stresses occurred with the lighter round wire. Conclusions: This first research on stresses on adjacent teeth during PIC traction provided needed quantitative data on the pattern of stress generation, suggesting the following clinical implications: use of distal-vertical pull from posterior anchorage (TPB) as initial movement and when using a buccal force, bypassing the lateral incisor and using heavier wires that would minimize side effects.

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The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automated, relies on the best fit of surfaces, not only rugal structures. In the first step, motion estimation requires segmenting and detecting the location of teeth and rugae at any time during the orthodontic intervention. Aim: to develop a process of tooth segmentation that eliminates all manual steps to achieve an autonomous system of assessment of the dentition. Methods: A dataset of 797 occlusal views from photographs of teeth was created. The photographs were manually semantically segmented and labeled. Machine learning methods were applied to identify a robust deep network architecture able to semantically segment teeth in unseen photographs. Using well-defined metrics such as accuracy, precision, and the average mean intersection over union (mIoU), four network architectures were tested: MobileUnet, AdapNet, DenseNet, and SegNet. The robustness of the trained network was additionally tested on a set of 47 image pairs of patients before and after orthodontic treatment. Results: SegNet was the most accurate network, producing 95.19% accuracy and an average mIoU value of 86.66% for the main sample and 86.2% for pre- and post-treatment images. Conclusions: Four architectural tests were developed for automated individual teeth segmentation and detection in two-dimensional photos that required no post-processing. Accuracy and robustness were best achieved with SegNet. Further research should focus on clinical applications and 3D system development.

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