Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation

The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = - 0.01 (10), mean difference = - 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 +/- 43.52 mm(3)) compared to desktop laser scanning (322.70 +/- 40.15 mm(3)). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68-0.87, sensitivity of 1.00, precision of 0.50-0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.

Automated summary

This study developed a 3D convolutional neural network to generate partial dental crowns for restorative dentistry. In phase 1, the effectiveness of desktop laser and intraoral scanners was evaluated, and intraoral scans were chosen for further analysis. In phase 2, tooth preparations were digitally synthesized and PDCs were designed using CAD workflows. The most accurate PDCs were then used to train the neural network in phase 3, leading to the development of a proof-of-concept 3D-CNN for generating PDCs in CAD.