Assessment of Cranial Implant Reconstruction Algorithms under Augmented Data Conditions

The majority of artificial intelligence (AI) modelling of cranial implant reconstruction is effective with in-domain raw datasets but can fail when considering new or altered conditions, such as augmented data. This study presents a comprehensive end-to-end evaluation of the most recent developments in algorithms for cranial implant reconstruction, with an emphasis on their adaptability, robustness, and capacity for generalization after training and testing on expanded and cross-domain datasets. A synthetic, control dataset of cranial defects has been generated using a range of augmentation strategies: intensity-based, geometric, and morphological to simulate real variability in patients’ response to treatment. Two different model architectures were evaluated, each included attention-enhanced 3D U-Nets and encoder-decoder networks, and each was structured to contain systematic benchmarking to allow analysis of how both model architecture and data augmentation influenced fidelity of reconstruction while preserving symmetry. Experimental results show that the 3D U-Net significantly outperformed the VAE, achieving a Dice score of 0.919 compared to 0.340, a Boundary Dice of 0.942 versus 0.296, and a substantially lower HD95 of 1.246 compared to 15.393. ... mehrAfter the 3D reconstruction stage, the study extended the evaluation to include exploring techniques for structural optimization by which to improve the anatomical fit and material efficiency, such as the strength-to-weight ratio, of implants. The optimized designs were then manufactured via 3D printing, and their real-world viability was assessed and validated using morphological and mechanical analyses. Such an integrated investigation was able to provide new insights into the interactions between the architecture of the model, data augmentation, and optimization of design, assisting the integrated development of intelligent, topology-aware fabrication of cranial implants.