Cranial Implant

Cranial implant design is shifting towards automation, aiming to create personalized implants faster and more efficiently for patients requiring cranioplasty. Current research heavily utilizes deep learning, employing architectures like U-Nets, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models to reconstruct cranial defects from medical images and generate implant designs. These methods often incorporate data augmentation techniques to address the variability of cranial defects and improve model generalizability. The ultimate goal is to streamline the implant creation process, reducing patient wait times and improving surgical outcomes.