Real-time intraoperative depth estimation in transsphenoidal surgery using deep learning: A feasibility study

Through the application of DINOv2, we were able to estimate depth in endoscopic imaging from transsphenoidal endonasal surgeries by generating numeric maps and depth colormaps. This illustrates the potential of deep learning-based depth estimations, which in the future could contribute to improving intraoperative orientation. It also highlights the opportunities in using artificial intelligence to augment endoscopic video feeds.