# DentalArch: AI-Based Arch Shape Detection in Orthodontics
## 4 Discussion  
Integrating AI-driven tools like DentalArch or similar tools into orthodontic practice for dental arch shape classification could offer significant enhancements. These tools not only ease the diagnostic process but also achieve precision and efficiency comparable to expert evaluations while reducing subjectivity. This is particularly valuable given the strong link between the dental arch shape and individual growth patterns [40,41]. By accurately assessing the arch shape, these tools can ensure treatment plans are aligned with each patient's unique developmental trajectory. Building upon this, the shift towards digital models in the field represents a paradigm shift, offering substantial savings in both time and resources. Digital models have emerged as a clinically viable alternative, embracing the digital transformation that encompasses computational intelligence, augmented reality, and three-dimensional printing technologies [42]. From the tests carried out with our data, DentalArch version 1 attained a 94.7% accuracy detecting the arch shape for the lower arch and a 93% accuracy for the upper arch. The more streamlined version 2, which assesses only four parameters, achieved an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. These results are in line with recent trends towards data-driven diagnostics in orthodontics, which were addressed by [PERSON] and [PERSON] [27] and [PERSON] and [PERSON] [28]. The use of machine learning, as explored in research by [PERSON] et al. [30] and [PERSON] et al. [31], underscores the potential for computational techniques to enhance diagnostic precision and reduce observer bias. When compared to established orthodontic software solutions like Dolphin Imaging & Management Solutions(r) and Nemote(r), DentalArch offers a novel, semiautomated approach to arch shape classification. Unlike these traditional platforms, which may require manual input for detailed analyses, DentalArch leverages AI to provide objective classifications with minimal user intervention. Furthermore, its open-source nature stands in contrast to the proprietary models of existing software, encouraging innovation and allowing for broader adaptation and improvement within the orthodontic community. DentalArch v2 relies on just four key input parameters for determining the dental arch form. This refinement stems from recent research emphasizing the critical role of specific arch measurements, particularly the intercanine width, in predicting dental arch shapes. [PERSON] et al. (2021) highlighted the strong link between the intercanine width and the forms of mandibular dental arches, validating the approach of using a subset of measurements for effective orthodontic analysis [43]. Motivated by such insights, DentalArch v2 was designed to enhance diagnostic precision by focusing on essential measurements like the intercanine width (ICW) and intermal width (IMW), corroborated by research [44,45], demonstrating their significant correlation with the overall arch form. This led to a substantial reduction in input parameters, simplifying the diagnostic workflow without compromising the prediction accuracy of dental arch shapes. The comprehensive evaluation of 29 machine learning methodologies for the development of DentalArch illustrates a strategic approach to enhancing orthodontic diagnostic tools. This analysis, which spanned from decision trees to neural network classifiers, was not only pivotal in identifying the most effective algorithms for arch shape prediction but also in maintaining the high diagnostic accuracy of DentalArch v2 despite its simplified model. The development of DentalArch, with an aim to apply it to orthodontic diagnostics, successfully balanced detailed accuracy and user convenience across lower and upper jaw analyses. While DentalArch v1's extensive parameters slightly edged out v2, the latter offered a compelling advantage, marking a minor compromise for significantly improved accessibility.