
DOI:
https://doi.org/10.14483/23448393.22383Published:
2025-06-15Issue:
Vol. 30 No. 1 (2025): January-AprilSection:
Biomedical EngineeringDevelopment and Validation of a Computer Vision-Based Tool for Automated 3D Dental Model Arch Prediction
Desarrollo y validación de una herramienta basada en visión por computadora para la predicción automatizada de la forma del arco dental en modelos 3D
Keywords:
3D Dental Models, Arch Form Analysis, Dental Morphometrics, Computer Vision, Orthodontic Automation (en).Keywords:
modelos dentales 3D, análisis de la forma del arco, morfometría dental, visión por computadora, automatización en ortodoncia (es).Downloads
Abstract (en)
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.
Abstract (es)
Contexto: La predicción precisa de la forma del arco dental es crucial para el tratamiento ortodóncico y la creación de aparatos dentales personalizados. Este estudio introduce una herramienta basada en visión por computadora para predecir las formas de arcos en modelos dentales digitales 3D.
Objetivo: Automatizar la selección de formas del arco dental mediante el análisis matemático de modelos. Método: De 484 modelos dentales digitales, se seleccionaron 50 mediante criterios específicos, que fueron clasificados por expertos en formas ovoides, cuadradas y cónicas usando plantillas 3M. Se desarrolló una herramienta de predicción automatizada de la forma del arco dental 3D, incorporando alineación automática, detección de cúspides, ajuste de curvas con un polinomio de sexto orden y comparación de modelos. Nuestras validaciones emplearon el análisis de concordancia de atributos, la raíz del error cuadrático medio, la suma de errores cuadráticos y un estudio Gage R&R.
Resultados: El estudio logró una tasa de concordancia del 90 % en la comparación de evaluador vs. estándar para la mandíbula inferior, además de 78 % para la mandíbula superior. El estudio Gage R&R afirmó la fiabilidad de la medición, y el modelo de polinomio de sexto orden fue identificado como óptimo para la descripción de la forma del arco. La precisión de la herramienta fue validada mediante análisis comparativo.
Conclusión: Esta investigación introduce un método automatizado para seleccionar formas de arcos dentales. La herramienta demostró una precisión sustancial, con potencial para mejorar significativamente los procesos de diagnóstico y planificación de tratamientos ortodóncicos. Futuras investigaciones podrían refinar esta metodología explorando otros modelos matemáticos e incorporando técnicas de aprendizaje automático.
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