Development and Validation of a Computer Vision-Based Tool for Automated 3D Dental Model Arch Prediction

Context: Accurate dental arch shape prediction is crucial for orthodontic treatment and personalized dental appliance creation. This study introduces a computer vision-based tool for predicting arch shapes in 3D dental models. Objective: To automate the selection of dental arch shapes through mathematical model analysis. Method: A dataset of 484 digital dental models was narrowed to 50 through specific criteria. Experts classified these into ovoid, square, and tapered shapes using 3M templates. An automated 3D dental arch shape prediction tool was developed, incorporating automatic alignment, cusp detection, curve fitting with a sixth-order polynomial, and model comparison. Our validations employed attribute agreement analysis, the root mean squared error, the sum of squared errors, and a Gage R&R Study. Results: This study achieved a 90% agreement rate in the evaluator vs. standard comparison for the lower jaw, as well as 78% for that of the upper jaw. The Gage R&R study confirmed measurement reliability, and the sixth-order polynomial model was identified as optimal for arch shape description. The tool’s predictive accuracy was validated through comparative analysis. Conclusion: This research introduces an effective automated method for selecting dental arch shapes. The tool demonstrated substantial accuracy, with the potential to significantly enhance orthodontic diagnostic and treatment planning processes. Future research could further refine this methodology by exploring advanced mathematical models and incorporating machine learning techniques to optimize the selection process.