Summary Objectives The aim of this retrospective study was to assess in maxillary canine impaction cases both the prevalence of root resorption of adjacent teeth among untreated children and adolescents, and its associated risk factors. Subjects and methods Sixty subjects (mean age 12.2 years; SD 1.9; range 8–17 years) with 83 displaced maxillary canines and without any past or ongoing orthodontic treatment were included in this study. The presence of root resorption was evaluated on images from a single cone beam computed tomography (CBCT) unit. Potential risk factors were measured on the CBCT images and on panoramic reconstructions of the 3D data sets. The sample was characterized by descriptive statistics and multiple logistic regressions were performed to predict root resorption. Results Root resorption of at least one adjacent tooth was detected in 67.5 per cent of the affected quadrants. It was found that 55.7 per cent of the lateral incisors, 8.4 per cent of the central incisors, and 19.5 per cent of first premolars were resorbed. Of the detected resorptions, 71.7 per cent were considered slight, 14.9 per cent moderate, and 13.4 per cent severe. Contact between the displaced canine(s) and the adjacent teeth roots was the only identified statistically significant risk factor, all teeth being considered (odds ratio [OR] = 18.7, 95% confidence interval: 2.26–756, P < 0.01). An enlarged canine dental follicle, a peg upper lateral, or an upper lateral agenesis were not significantly associated with root resorption of adjacent teeth, nor were age nor gender. Conclusions Root resorption of adjacent teeth was detected in more than two-thirds of a sample of sixty untreated children and adolescents.

The aim of this systematic review was to assess the accuracy and reliability of automatic landmarking for cephalometric analysis of 3D craniofacial images. We searched for studies that reported results of automatic landmarking and/or measurements of human head CT or CBCT scans in MEDLINE, EMBASE and Web of Science until march 2019.Two authors independently screened articles for eligibility. Risk of bias and applicability concerns for each included study were assessed using the QUADAS-2 tool. Eleven studies with test dataset sample sizes ranging from 18 to 77 images were included. They used knowledge-, atlas-or learning-based algorithms to landmark 2 to 33 points of cephalometric interest. Ten studies measured mean localization errors between manually-and automatically-detected landmarks. Depending on the studies and the landmarks, mean errors ranged from <0.50 mm to >5 mm. The two best-performing algorithms used a deep learning method and reported mean errors <2 mm for every landmark, approximating results of operator variability in manual landmarking. Risk of bias regarding patient selection and implementation of the reference standard were found, therefore the studies might have yielded overoptimistic results. The robustness of these algorithms needs to be more thoroughly tested in challenging clinical settings. PROSPERO registration number: CRD42019119637.

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set ( n = 160) and a test set ( n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator( n = 178) or twice by 3 operators ( n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

In some dentofacial deformity patients, especially patients undergoing surgical orthodontic treatments, Computed Tomography (CT) scans are useful to assess complex asymmetry or to plan orthognathic surgery. This assessment would be made easier for orthodontists and surgeons with a three-dimensional (3D) cephalometric analysis, which would require the localization of landmarks and the construction of reference planes. The objectives of this study were to assess manual landmarking repeatability and reproducibility (R&R) of a set of 3D landmarks and to evaluate R&R of vertical cephalometric measurements using two Frankfort Horizontal (FH) planes as references for horizontal 3D imaging reorientation. Thirty-three landmarks, divided into “conventional”, “foraminal” and “dental”, were manually located twice by three experienced operators on 20 randomly-selected CT scans of orthognathic surgery patients. R&R confidence intervals (CI) of each landmark in the -x, -y and -z directions were computed according to the ISO 5725 standard. These landmarks were then used to construct 2 FH planes: a conventional FH plane (orbitale left, porion right and left) and a newly proposed FH plane (midinternal acoustic foramen, orbitale right and left). R&R of vertical cephalometric measurements were computed using these 2 FH planes as horizontal references for CT reorientation. Landmarks showing a 95% CI of repeatability and/or reproducibility > 2 mm were found exclusively in the “conventional” landmarks group. Vertical measurements showed excellent R&R (95% CI < 1 mm) with either FH plane as horizontal reference. However, the 2 FH planes were not found to be parallel (absolute angular difference of 2.41°, SD 1.27°). Overall, “dental” and “foraminal” landmarks were more reliable than the “conventional” landmarks. Despite the poor reliability of the landmarks orbitale and porion, the construction of the conventional FH plane provided a reliable horizontal reference for 3D craniofacial CT scan reorientation.

The increasing use of three-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform tedious manual landmarking. In this study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted in 33 landmarks, manually localized once by 1 operator (n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, one CT scan showed "very low" confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3mm, while success detection rates for 2.0, 2.5 and 3.0mm were 90.4%, 93.6% and 95.4%, respectively. Mean errors were -0.3 ± 1.3° and -0.1 ± 0.7mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland-Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

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Aims To assess in each European country the correlation between the number of Network of Erasmus‐Based European Orthodontic Postgraduate Programmes (NEBEOP) members and orthodontic research activity. Secondary objectives were to describe and quantify Europe's orthodontic research. Materials and methods Articles published between 2014 and 2018 in 4 major orthodontic journals (American Journal of Orthodontics and Dentofacial Orthopedics, European Journal of Orthodontics, The Angle Orthodontist, Orthodontics and Craniofacial Research) and oral presentation abstracts of five European Orthodontic Society (EOS) congresses were analysed. For each European country, the total number of orthodontic programmes and NEBEOP memberships were collected. Descriptive statistics were performed, and Spearman correlation coefficients and risk ratios were calculated. Results 2039 articles and 261 oral presentation abstracts were included. Correlation coefficients between national number of publications, oral presentations, sum of these, all adjusted for population, and number of NEBEOP members in each country were 0.64, 0.65 and 0.62, respectively. Risk ratios were all above 1 and statistically significant for number of NEBEOP memberships per country, indicating positive associations with national orthodontic research productivity. Europe accounted for 30.5% of publications and 68.6% of oral presentations at EOS congresses during this period. European orthodontic research was not evenly distributed, since 9 countries were responsible for around 80% of the output. Conclusions A positive association was found between number of NEBEOP programmes and national research activity. These results could be an additional argument to support similar pan‐European initiatives and guidelines for postgraduate education, not only in orthodontics but in all other dental specialties.