Investigating AI-Driven Geometric Modeling for Craniosynostosis Surgical Planning

This paper investigates the intersection of artificial intelligence (AI) and geometric modeling in the surgical management of craniosynostosis. We evaluate the transition from subjective, manual planning to data-driven, algorithmic workflows for cranial vault remodeling (CVR), assessing the efficacy of deep learning in automating diagnosis, 3D segmentation, shape reconstruction, and predictive planning. While diagnostic algorithms often report high accuracy (commonly in the high-80s to mid-90s% range, depending on modality and dataset) [59, 77, 84], substantial gaps remain in automated planning, particularly regarding data scarcity, anatomical constraint integration, and the lack of standardized evaluation metrics. We discuss a roadmap for future research, prioritizing hybrid AI-physics surrogates, federated learning frameworks, and differentiable geometric optimization to achieve standardized, patient-specific surgical outcomes.