Application of artificial intelligence methods in orbital segmentation and volume analysis. Issues of comparative analysis

Background Research in the field of orbital imaging demonstrates that AI methods, in particular deep learning architectures, can outperform traditional manual segmentation [1–5]. Such approaches provide higher accuracy of orbital structure extraction, significantly reducing interobserver variability. Aim To evaluate and compare the performance of modern AI segmentation methods with traditional manual approaches for orbital structure extraction and volumetric analysis. Material and Methods The study is based on the analysis of scientific literature and published studies. Results Preliminary results indicate that AI segmentation methods significantly outperform traditional manual segmentation in terms of accuracy and reproducibility, reducing interobserver variability. Automated voxel counting combined with AI segmentation significantly reduces the time required for volumetric analysis, which has a positive impact on the clinical workflow. In addition, the achieved improvements contribute to more accurate diagnostics and assist in surgical planning, which confirms their clinical significance. The key success factors are: • availability of high-quality, well-annotated datasets, standardized multi-sequence imaging; • modern computing infrastructure (GPU, specialized frameworks) for training and integrating algorithms into the clinical workflow; • interdisciplinary collaboration between specialists to fine-tune the algorithms and confirm their clinical accuracy. All these components together contribute to the development of more accurate treatment strategies and potentially improve patient outcomes. Conclusion The integration of modern AI architectures (nnU-Net, Attention-Based Neural Networks) with automated voxel counting methods represents a significant breakthrough in orbital segmentation and volumetric analysis. These methods not only provide high accuracy, but also contribute to improving the quality of surgical treatment planning. Keywords: orbital measurements; segmentation; volumetric analysis; artificial intelligence; surgical planning