GENERATIVE AI FOR 3D RECONSTRUCTION AND SIMULATION OF COMPLEX DENTAL SURGERIES - Pawar, Mahendra Eknath (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Advancements in generative artificial intelligence (AI) have unlocked new possibilities in the field of dental surgery, particularly in the reconstruction and simulation of complex surgical procedures. This paper presents a novel framework leveraging generative models—such as Generative Adversarial Networks (GANs) and diffusion models—for 3D anatomical reconstruction from limited 2D imaging data, enabling accurate and realistic simulation of maxillofacial surgical interventions. The system incorporates patient-specific data, including cone-beam computed tomography (CBCT) scans and intraoral photographs, to generate high-fidelity 3D models of craniofacial structures. These models are further used to simulate various surgical scenarios, assisting clinicians in preoperative planning, risk assessment, and patient-specific surgical rehearsal. The integration of physics-based constraints and AI-driven biomechanical modeling improves the realism of simulated tissue responses during interventions. Experimental results demonstrate the system’s potential in enhancing surgical precision, reducing operative time, and improving overall patient outcomes. The study underscores the transformative impact of generative AI in dental surgery, paving the way for more personalized, safe, and effective treatment planning.