325 3D Skull Base Reconstruction Using Publicly Available Foundational AI Models and Endoscope Video

Abstract

INTRODUCTION: The ablative nature of surgery means that pre-operative imaging studies lose correspondence as a case progresses, which can be problematic when accurate intraoperative navigation is required. Accurate 3D surface reconstruction from endoscopic video is a potential strategy for real-time intraoperative imaging updates without additional equipment. We have previously used traditional computational models to generate skull base reconstructions. However, they are time-consuming and require technical skills to process the video. Recent foundational AI models, like DUST3R, are an opportunity for timely, generalizable reconstructions of surgical anatomy. METHODS: We compared our previously described three-step reconstruction process with DUST3R to generate a water-tight 3D mesh of cadaveric skull base anatomy visualized using a Karl Storz Image 1 Hub HD Video Camera fitted with a 0° rigid endoscope. For DUST3R, we selected four video frames and did not perform any training or fine-tuning. RESULTS: Without endoscope calibration and using only the four input frames, DUST3R created 3D surface reconstructions in less than two minutes. This is compared to the three-step reconstruction process, which requires 8 to 12 hours to reconstruct. CONCLUSIONS: Our findings show that DUST3R, a publicly available foundational AI model, can rapidly generate 3D anatomical reconstructions from a limited set of video frames. Models like DUST3R illustrate the rapidly evolving potential of computer vision. With fine-tuning, they may represent a path toward foundational AI models that generalize across surgical procedures.

Ähnliche Arbeiten