Accuracy and efficiency of artificial intelligence and manual virtual segmentation for generation of 3D-printed tooth replicas

doi:10.3290/j.ijcd.b6329209

Q3 · DentistryImpact Factor · 1.5

AutoresIgnacio Pedrinaci, Amirali Nasseri, Javier Calatrava, Emilio Couso-Queiruga, William V. Giannobile, German O. Gallucci, Mariano Sanz

Por qué importa

Un estudio reciente compara la precisión y la eficiencia de la segmentación virtual asistida por inteligencia artificial (IA) frente a métodos manuales para la creación de réplicas dentales impresas en 3D. La investigación evaluó las discrepancias volumétricas y lineales obtenidas con cada técnica, así como el tiempo empleado en la segmentación y el impacto del postprocesado en las réplicas. Los resultados sugieren que, si bien la segmentación manual mostró una ligera mayor precisión, la IA ofrece una notable mejora en la eficiencia temporal. Estos hallazgos son relevantes para la práctica clínica, indicando el potencial de la IA para optimizar flujos de trabajo en la planificación y fabricación de modelos dentales, facilitando diagnósticos más precisos y tratamientos personalizados mediante la impresión 3D.

Abstract original

AIM: The primary aim of the present in vitro study was to compare methods for generating 3D-printed replicas through virtual segmentation, utilizing artificial intelligence (AI) or manual processes, by assessing accuracy in terms of volumetric and linear discrepancies. The secondary aims were the assessment of time efficiency with both segmentation methods, and the effect of post-processing on 3D-printed replicas. MATERIALS AND METHODS: Thirty teeth were scanned through CBCT, capturing the region of interest from human subjects. DICOM files underwent virtual segmentation through both AI and manual methods. Replicas were fabricated with a stereolithography 3D printer. After surface scanning of pre-processed replicas and extracted teeth, STL files were superimposed to compare linear and volumetric differences using the extracted teeth as the reference. Post-processed replicas were scanned to assess the effect of post-processing on linear and volumetric changes. RESULTS: AI-driven segmentation resulted in statistically significant mean linear and volumetric differences of -0.709 mm (SD 0.491, P 0.001) and -4.70%, respectively. Manual segmentation showed no statistically significant differences in mean linear (-0.463 mm, SD 0.335, P 0.001) and volumetric (-1.20%) measures. Comparing manual and AI-driven segmentations, AI-driven segmentation displayed mean linear and volumetric differences of -0.329 mm (SD 0.566, P = 0.003) and -2.23%, respectively. Additionally, AI segmentation reduced the mean time by 21.8 minutes. When comparing post-processed to pre-processed replicas, there was a volumetric reduction of -4.53% and a mean linear difference of -0.151 mm (SD 0.564, P = 0.042). CONCLUSIONS: Both segmentation methods achieved acceptable accuracy, with manual segmentation slightly more accurate but AI-driven segmentation more time-efficient. Continual improvement in AI offers the potential for increased accuracy, efficiency, and broader application in the future.

Leer texto completo ↗DOI · 10.3290/j.ijcd.b6329209

Autores Doctor&Cols

Cómo citar

Pedrinaci I, Nasseri A, Calatrava J, Couso-Queiruga E, Giannobile WV, Gallucci GO, et al. Accuracy and efficiency of artificial intelligence and manual virtual segmentation for generation of 3D-printed tooth replicas. International Journal of Computerized Dentistry. 2026. doi:10.3290/j.ijcd.b6329209