Role of prosthetic parameters for validating an AI designed crown

Aim : To investigate the relationship between the angles describing the emergence profile of an implant crown and the metric data used to validate the digital design modeled via a 3D GAN. Materials and methods : Twenty crowns were generated for the study using 3D GAN. Their validation metrics, including IoU (intersection over union), F1 score, precision, and recall, were recorded. The initial voxelized files were processed with MeshMixer software, followed by the registration of longitudinal cuts using ExoCad software. CorelDRAW was used for outlining. Three angles were then measured for each examined surface—the mucosal emergence angle (MEA), the deep emergence angle (DA), and the total contour angle (CA)—resulting in a total of 240 measured angles. Results : The mean values of the angles were MEA=27.79°, DA=56.70°, and CA=43.54°, with maximum values of 68.78°, 93.55°, and 83.01°, and minimum values of 0.01°, 27.51°, and 12.35°, respectively. The overall median IoU was 0.835, while the median precision, recall, and F1 score were 0.946, 0.942, and 0.91, respectively, across the 20 teeth. Conclusion : The validation of AI must evolve to include clinical parameters beyond digital metrics. Visual parameters used to validate the design of implant crowns are not universal but rather supplementary measures for assessing the real clinical value of newly generated crowns. The present findings indicate that even in cases where the IoU, precision, recall, and F1 scores are not perfect (<1), clinically significant parameters such as MEA can still demonstrate an excellent mean value (27.79°), which is a predictive factor for peri-implant tissue health.