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- age determination by skeleton (1)
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- deformational cranial asymmetry (1)
- infants with deformational plagiocephaly (DP) (1)
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- Poliklinik für Kieferorthopädie (2) (remove)
Objectives
The present investigation aimed to evaluate the subjective perception of deformational cranial asymmetries by different observer groups and to compare these subjective perceptions with objective parameters.
Materials and methods
The 3D datasets of ten infants with different severities of deformational plagiocephaly (DP) were presented to 203 observers, who had been subdivided into five different groups (specialists, pediatricians, medical doctors (not pediatricians), parents of infants with DP, and laypersons). The observers rated their subjective perception of the infants’ cranial asymmetries using a 4-point Likert-type scale. The ratings from the observer groups were compared with one another using a multilevel modelling linear regression analysis and were correlated with four commonly used parameters to objectively quantify the cranial asymmetries.
Results
No significant differences were found between the ratings of the specialists and those of the parents of infants with DP, but both groups provided significantly more asymmetric ratings than did pediatricians, medical doctors, or laypersons. Moreover, the subjective perception of cranial asymmetries correlated significantly with commonly used parameters for objectively quantifying cranial asymmetries.
Conclusions
Our results demonstrate that different observer groups perceive the severity of cranial asymmetries differently. Pediatricians’ more moderate perception of cranial asymmetries may reduce the likelihood of parents to seek therapeutic interventions for their infants. Moreover, we identified some objective symmetry-related parameters that correlated strongly with the observers’ subjective perceptions.
Clinical relevance
Knowledge about these findings is important for clinicians when educating parents of infants with DP about the deformity.
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.