## 3 Discussion
A recent literature review by [PERSON] et al. [17] from 2024 examines measurement studies based on digital impressions. [PERSON] et al. [26] found no significant dimensional differences in cleft width and alveolar ridge width measurements between 3D-printed models, digital models generated by IOS, and plaster models obtained through conventional impressions. Similarly, a study by [PERSON] et al. [31] from 2024 reported no differences between 3D-printed models and plaster models for the same measurement distances. \"A narrative review on non-invasive diagnostic tools for the analysis of dental arches in orofacial cleft patients\" by [PERSON] et al. [13] from 2022 concluded that digital measuring tools, which evaluate the dental arches of children with CLP, are reproducible and reliable. The studies presented, which are comparable to the measurement method we outlined, were based on the digital measurement of digitized plaster models. [PERSON] et al. [32] evaluated in 2022 the volume of the dental arches in children with unilateral cleft lip and palate on digitized dental casts. [PERSON] et al. [33] measured in 2021 the surface changes in digitized plaster casts. The various studies evaluating growth changes in the maxilla of patients with unilateral cleft lip and palate differ in the methodology of model acquisition, the number and nomenclature of measurement points, and the type of measurement used. Therefore, the choice of terms and the selection of anatomical points presented in this study do not constitute a general justification. We selected these points to ensure they are easily identifiable and accurately represent the features we wish to measure. To provide an accurate and generally comparable representation, the respective anatomical description is given. The digital analysis presented could also be supplemented with several additional useful points, measuring distances and angles. Therefore, it would be desirable in the future to establish a uniform digital measurement analysis with a consistent number of standardized, named, and defined measurement points, measuring distances and angles, across specialized centers in order to make the upper jaw measurements of patients with unilateral cleft lip and palate more comparable. A full digital workflow including digital model analysis is increasingly relevant for future directions, particularly due to its potential to integrate artificial intelligence (AI) and automate landmark identifications. As AI technologies advance, they can enhance the accuracy and efficiency of identifying and measuring critical anatomical structures, reducing the subjectivity and variability that can arise from manual landmark positioning. Automated landmark detection can streamline the analysis process, making it not only faster but also consistent, while AI algorithms can learn from large center-wide data sets to improve over time. This integration can significantly enhance the precision of digital model assessments, providing research with reliable, reproducible results that are essential for long-term studies and clinical applications, particularly in complex rehabilitations like in cleft lip and palate patients. Moreover, the ability to process large amounts of data can facilitate large-scale, longitudinal studies, fostering a deeper understanding of growth patterns and treatment outcomes in patients with cleft lip and palate.