Neurosurgery 4.0: Automated 3D Anatomical Modeling, AI-Driven Segmentation, and Next-Generation Surgical Planning

The field of neurosurgery is undergoing a rapid digital transformation fueled by the convergence of artificial intelligence (AI), advanced imaging analytics, and computational modeling. Traditional neurosurgical planning has relied heavily on manual segmentation of 2D and 3D radiological data—a process that is labor-intensive, time-consuming, and heavily dependent on individual operator expertise. With high-resolution multimodal imaging generating enormous datasets, the need for scalable, automated solutions has become increasingly critical. Despite the technological advances in 3D visualization and intraoperative navigation, challenges remain in integrating these automated tools into routine clinical workflows and validating their safety and accuracy. Current studies highlight the promising potential of deep learning (DL) architectures, such as convolutional neural networks and vision transformers, which achieve near-human performance in anatomical segmentation and offer substantial time savings. Yet, clinical validation, interoperability standards, and algorithm fairness continue to represent key unmet needs.This Research Topic aims to accelerate the development and clinical adoption of fully automated neurosurgical modeling pipelines—from acquisition of raw DICOM data to 3D reconstruction, extended reality visualization, and customized surgical simulation. By collecting interdisciplinary research at the interface of computer science, biomedical engineering, and clinical neurosurgery, it seeks to advance understanding of how AI can augment surgical judgment and improve preoperative decision-making. The goal is to identify the most effective algorithmic frameworks, validate their performance in diverse patient cohorts, and explore how automation can democratize access to high-fidelity surgical planning beyond specialized centers.The scope of this Research Topic encompasses both foundational algorithmic advances and translational clinical studies. It encourages submissions focused on scalable, reproducible, and clinically validated technologies that enhance surgical safety and efficiency. To gather further insights into the capabilities and limitations of automated modeling in neurosurgery, we welcome articles addressing, but not limited to, the following themes:-AI-based segmentation of tumors, edema, and critical neurovascular structures-Automated fiber tractography and reconstruction of eloquent white matter pathways-Multi-modal fusion and image registration for integrative surgical mapping-3D printing, bioprinting, and mesh optimization for physical surgical modeling-XR-based applications for AR/VR intraoperative visualization and preoperative rehearsal-Clinical validation of automation pipelines: accuracy, reproducibility, and outcomes-Generative AI approaches for improving image quality and data augmentation Topic editors: Ilkan Tatar, Sahin Hanalioglu Source: https://www.frontiersin.org/research-topics/78330/neurosurgery-40-automated-3d-anatomical-modeling-ai-driven-segmentation-and-next-generation-surgical-planning