# DentalArch: AI-Based Arch Shape Detection in Orthodontics
## 1 Introduction
Early methods for classifying arch shapes depended heavily on manual measurements and subjective evaluations, introducing considerable variability in results. However, the rise of AI and machine learning models has begun to change this landscape, offering innovative insights and methods for arch shape classification. [PERSON] and [PERSON] [27] utilized mathematical methods to categorize variations in dental arch shape among an Australian Aboriginal population, highlighting the diversity in arch forms and the challenges in their classification. [PERSON] and [PERSON] [28] explored the correlation between subjective classifications of dental arch shape and objective analyses, shedding light on potential discrepancies and the need for more precise methodologies. [PERSON] et al. [29] proposed a novel method to classify dental arch forms, employing clustering algorithms to sort arch shapes based on their goodness of fit and clinical applicability, demonstrating a shift towards more objective, data-driven classification methods. [PERSON] et al. [30] developed an AI system using a convolutional neural network (CNN) to classify partially edentulous arches, marking a significant advancement in the AI-assisted design of removable partial dentures. Their system achieved high diagnostic accuracies, showcasing the potential of AI in dental arch classification. [PERSON] et al. [31] introduced DArch, a dental arch prior-assisted method for 3D tooth instance segmentation. By leveraging the dental arch structure, they enhanced the accuracy of tooth segmentation in 3 Ddental models, emphasizing the value of incorporating dental arch priors into AI models for orthodontic applications. In addition to these research efforts, commercial software solutions like Dolphin Imaging & Management Solutions(r) and Nemote(r) have incorporated tools for dental arch analysis within their orthodontic planning and imaging suites [32]. While offering sophisticated imaging and diagnostic capabilities, it is important to note that these programs facilitate assisted rather than fully automatic arch shape detection. Practitioners use these tools to analyze and evaluate dental arches, but expert input is required to interpret and apply the software's findings in treatment planning. This distinction underscores the ongoing need for advancements in AI that can further automate and refine the process of arch shape classification and that aim to reduce the reliance on manual assessments and subjective evaluations. These contributions collectively highlight the significant impact of AI on orthodontic arch shape classification and prediction, with \"DentalArch\" and similar AI-driven tools offering alternatives to traditional methods, alongside the assisted capabilities provided by existing commercial software. The remainder of this document outlines the study structure, covering related work; materials and methods, encompassing data collection, arch form classification, software development, and machine learning technique evaluation; the main results attained with the tool; a brief discussion; and the principal conclusions drawn from our findings.