# DentalArch: AI-Based Arch Shape Detection in Orthodontics
## 2 Materials and Methods
### Tool Development  
The development of the DentalArch tool, tailored for arch shape detection, resulted in two distinctive versions:  
* DentalArch v1: Incorporating a detailed set of 18 input parameters--comprising mesiodistal widths for each tooth from the first molar on one side to the first molar on the opposite side (14 parameters), along with widths for 1 intercanine, 2 interremolars, and 1 intermolar--this version offers an exhaustive approach to analyzing arch shapes. This comprehensive parameter set facilitates a nuanced assessment of dental arches.
* DentalArch v2: This iteration simplifies the input to just 4 critical parameters--1 intercanine width, 2 interremolar widths, and 1 intermolar width. With a singular output parameter, DentalArch v2 simplifies the diagnostic process, emphasizing an ease of use and practicality without compromising the accuracy of its outputs. This version is designed for efficiency, making it particularly suitable for quick assessments while retaining the reliability expected of orthodontic diagnostic tools.  
Both versions were developed using MATLAB(r) R2023B, ensuring a robust and efficient implementation. This choice of platform not only secures a high-performance tool solution but also promotes compatibility and seamless execution across diverse systems [36].  
An essential feature of DentalArch is its user-friendly interface, allowing orthodontic professionals to manually input the necessary measurements. This intentional user-centric approach enhances the software's accessibility and ensures its seamless integration into the clinical practices of orthodontic professionals.  
Furthermore, DentalArch exhibits Windows compatibility, being executable on any Windows-based computer, enabling orthodontic professionals to incorporate DentalArch into their practice without concerns about operating system constraints.  
In order to develop DentalArch, a comprehensive analysis of 29 different machine learning techniques was conducted. These techniques were systematically evaluated to determine the most suitable method for the task of classifying dental arch shapes. The selection process aimed to identify a technique that could provide high accuracy in this classification task.  
The machine learning techniques were categorized into various groups, including decision trees, discriminant analysis, naive Bayes classifiers, support vector machines, nearest neighbor classifiers, ensemble classifiers, and neural network classifiers. These categories were chosen to encompass a wide range of methodologies and capture diverse approaches to classification.  
The performance of each technique was assessed based on its accuracy in classifying dental arch shapes. By comparing the results obtained from these techniques, we were able to identify the most effective approach for the development of DentalArch.  
Table 2 provides a summary of the machine learning techniques analyzed in this study, along with their respective categories.  
Figure 2: MeshLab measurement examples: (**A**) shows the dental model with measurements captured in MeshLab, and (**B**) provides an alternate view of the same model with measurements. Note: The arch shape parameter was determined using conventional assessment methods.