Pioneering fully automated bony orbit segmentation: an in silico nnU-Net multicentre approach.

In maxillofacial surgery, orbital reconstruction requires precision to address both functional and aesthetic considerations arising from both acute and elective conditions. This study presents a novel, fully automated segmentation software designed specifically for the orbital floor. This software enhances surgical planning through superior accuracy, efficiency, and usability. The authors' transdisciplinary team compiled a dataset of 1004 expert-segmented orbits from computed tomography images across multiple countries, ensuring broad anatomical representation. Developed with the nnU-Net framework, the software achieved segmentation accuracy with a mean Dice similarity coefficient of 0.935 and a mean surface distance of 0.292 mm for the orbit, and a Dice similarity coefficient of 0.917 and mean surface distance of 0.287 mm for the orbital floor-all within approximately 1 s. This performance surpasses traditional manual segmentation, which averages 25 min per orbit. The system delivered consistent results across a range of imaging sources, affirming its reliability for a wide range of clinical applications. By introducing this fully automated, high-precision tool, this study pioneers advancements in AI-driven orbital reconstructions, setting new standards for patient-specific surgical planning. Further development and integration holds the key to transforming the field, ensuring ethical compliance and enhancing informed consent.