As there are very few long-term studies on the effects of head orthosis on deformational plagiocephaly (DP), we investigated the outcomes of patients, including facial symmetry and dental occlusion. Forty-five infants with DP [cranial vault asymmetry index (CVAI) > 3.5 per cent] were divided into two groups: one treated with head orthosis (32 infants) and another without (13 infants). Another group without head asymmetry (CVAI ≤ 3.5 per cent) served as control. Using 3D-stereophotogrammetry, cranial asymmetry was analysed using symmetry-related variables [CVAI, posterior cranial asymmetry index (PCAI), and ear offset]. Data acquisition was performed before (T1) and at the end of treatment (T2), and at the age of 4 years (T3) for the treated group and at T1 and T3 for the remaining groups. Parameters of facial symmetry and dental occlusion were assessed at T3 for infants with DP. Symmetry-related variables (∆T1−T3) improved significantly more in the treated than the control group, whereas these parameters did not differ significantly between the untreated and control group. Comparing the treated and untreated groups between T1 and T3, the reduction in the asymmetry at the treated group was significantly higher for the CVAI and PCAI. In follow-up, the untreated group showed higher incidences of facial asymmetries than the treated group. Seventy-five per cent of all lateral crossbites found in patients with DP were contralateral to the posterior flattening. Due to ethical reasons, the investigation is a non-randomized study. Parameters of facial symmetry were only assed for the treated and untreated groups. Head orthosis therapy in patients with DP leads to significantly better long-term outcomes. Facial asymmetries are more frequent in patients with DP who do not receive this treatment.

Purpose: The aim of this investigation was to evaluate the accuracy of various skeletal and dental cephalometric parameters as produced by different commercial providers that make use of artificial intelligence (AI)-assisted automated cephalometric analysis and to compare their quality to a gold standard established by orthodontic experts. Methods: Twelve experienced orthodontic examiners pinpointed 15 radiographic landmarks on a total of 50 cephalometric X‑rays. The landmarks were used to generate 9 parameters for orthodontic treatment planning. The “humans’ gold standard” was defined by calculating the median value of all 12 human assessments for each parameter, which in turn served as reference values for comparisons with results given by four different commercial providers of automated cephalometric analyses (DentaliQ.ortho [CellmatiQ GmbH, Hamburg, Germany], WebCeph [AssembleCircle Corp, Seongnam-si, Korea], AudaxCeph [Audax d.o.o., Ljubljana, Slovenia], CephX [Orca Dental AI, Herzliya, Israel]). Repeated measures analysis of variances (ANOVAs) were calculated and Bland–Altman plots were generated for comparisons. Results: The results of the repeated measures ANOVAs indicated significant differences between the commercial providers’ predictions and the humans’ gold standard for all nine investigated parameters. However, the pairwise comparisons also demonstrate that there were major differences among the four commercial providers. While there were no significant mean differences between the values of DentaliQ.ortho and the humans’ gold standard, the predictions of AudaxCeph showed significant deviations in seven out of nine parameters. Also, the Bland–Altman plots demonstrate that a reduced precision of AI predictions must be expected especially for values attributed to the inclination of the incisors. Conclusion: Fully automated cephalometric analyses are promising in terms of timesaving and avoidance of individual human errors. At present, however, they should only be used under supervision of experienced clinicians.

Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.