Interactive Holographic Reconstruction of Dental Structures: A Review and Preliminary Design of the HoloDent3D Concept

Panoramic radiography remains a cornerstone diagnostic tool in dentistry; however, its two-dimensional nature limits the visualisation of complex maxillofacial anatomy. Three-dimensional reconstruction from single panoramic images addresses this limitation by computationally generating spatial representations without additional radiation exposure or expensive cone-beam computed tomography (CBCT) scans. This systematic review and conceptual study traces the evolution of 3D reconstruction approaches, from classical geometric and statistical shape models to modern artificial intelligence-based methods, including convolutional neural networks, generative adversarial networks, and neural implicit fields such as Occudent and NeBLa. Deep learning frameworks demonstrate superior accuracy in reconstructing dental and jaw structures compared to traditional techniques. Building on these advancements, this paper proposes HoloDent3D, a theoretical framework that combines AI-driven panoramic reconstruction with real-time holographic visualisation. The system enables interactive, radiation-free volumetric inspection for diagnosis, treatment planning, and patient education. Despite significant progress, persistent challenges include limited paired 2D–3D datasets, generalisation across anatomical variability, and clinical validation. Continued integration of multimodal data fusion, temporal modelling, and holographic visualisation is expected to accelerate the clinical translation of AI-based 3D reconstruction systems in digital dentistry.