Development of an Application for Evaluating Tooth Positions in Orthodontic Cases based on ABO Guideline

An accurate orthodontic assessment is fundamental to the treatment planning stage within evidence-based dentistry, but currently established diagnostic workflows are still largely reliant on human observations of the orthodontal diagnostic parameters. While these assessed workflows are widespread, they are often laborious, subjective, and prone to human error. In the present study, we present an automated orthodontic evaluation system informed by the Objective Grading System by the American Board of Orthodontics. The proposed scheme automatically analyzes three-dimensional STL dental models to extract the diagnostic parameters; midline displacement, overjet, occlusion, and axial inclination, through repeated and standardized computational procedures, ensuring adherence to the standards developed by ABO. Machine learning models are used for the analysis and scoring, with administrators able to collect, quantify, and categorize the parameters in a significantly more objective and reproducible way. The end-user interface (GUI), is simple to use, allowing clinicians to input STL files and receive automated scoring and visual feedback in real time. Results indicate decreased inter-examiner variability, more precise diagnostic assessments, and clinical support toward evidence-based practice. Overall, this work provides a validated digital platform and readily accessible digital tool that would probably automate the orthodontist's chair-side evaluation process.