Cranial Defect

Cranial defects, encompassing craniofacial anomalies and injuries requiring cranial implants, are a significant clinical challenge addressed by ongoing research focusing on automated diagnosis and personalized treatment. Current research heavily utilizes deep learning, employing architectures like U-Nets, variational autoencoders, generative adversarial networks, and diffusion models to reconstruct cranial defects from medical images, often incorporating data augmentation techniques to improve model generalizability. These advancements aim to accelerate and improve the design and manufacturing of personalized cranial implants, potentially reducing patient wait times and improving surgical outcomes, while also enhancing the accuracy and efficiency of craniofacial anomaly diagnosis.