Volumetric Feature Learning for High-Fidelity Two-Dimensional Dental Cast Image Reconstruction Using Generative Adversarial Networks (GANs)

Dentistry is a medical branch that diagnoses and treats oral diseases, helps maintain oral function, and improves oral aes-thetics. Dental casts are three-dimensional models of a patient’s oral tissues that can be used to study oral anatomy, assess oc-clusal relationships, and determine tooth alignment. Traditional-ly, they were made of gypsum, an impression material used to pour into the patient’s mouth molds. Meanwhile, digital ones are three-dimensional models generated virtually using modern digi-tal imaging and intraoral scanners. Unlike physical models, which require a lot of manual work and ample storage space, digital models can be produced rapidly, easily modified, and stored for long-term usage. In this study, we present Denta-RecGAN, a novel approach based on Generative Adversarial Networks (GANs) that maps a two-dimensional dental cast im-age into a volumetric latent space and projects it back into a two-dimensional output. The proposed approach employs a 2D encoder to process dental cast images as input, enabling the extraction of spatial features. The structural depth is modelled, and noise is suppressed using volumetric 3D latent space de-noising models; a 2D decoder then reconstructs a high-quality image. The model is trained under an adversarial learning ap-proach using the IO150K dataset. The proposed architecture achieved Mean Absolute Error (MAE) of 0.0128, 0.0127, 0.0128; Structural Similarity Index Measure (SSIM) of 0.9450, 0.9452, 0.9453; and Peak Signal-to-Noise Ratio (PSNR) of 28.84, 28.85, 28.84?decibels across training, validation, and testing sets. These results demonstrate the effectiveness of volumetric feature learning in enhancing the accuracy of 2D image re-construction and preserving fine structural details.

@article{Eldaoushy2026,
  title     = {Volumetric Feature Learning for High-Fidelity Two-Dimensional Dental Cast Image Reconstruction Using Generative Adversarial Networks (GANs)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Eman Ahmed Eldaoushy and Manal A. Abdel-Fattah and Nermeen Ahmed Hassan and Mai M. El defrawi},
  doi       = {10.14569/IJACSA.2026.0170148},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170148}
}