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Purpose: In this paper, we present a novel approach to the automaticevaluation of open surgery skills using depth cameras. This work is intended toshow that depth cameras achieve similar results to RGB cameras, which is thecommon method in the automatic evaluation of open surgery skills. Moreover,depth cameras offer advantages such as robustness to lighting variations,camera positioning, simplified data compression, and enhanced privacy, makingthem a promising alternative to RGB cameras.  Methods: Experts and novice surgeons completed two simulators of opensuturing. We focused on hand and tool detection, and action segmentation insuturing procedures. YOLOv8 was used for tool detection in RGB and depthvideos. Furthermore, UVAST and MSTCN++ were used for action segmentation. Ourstudy includes the collection and annotation of a dataset recorded with AzureKinect.  Results: We demonstrated that using depth cameras in object detection andaction segmentation achieves comparable results to RGB cameras. Furthermore, weanalyzed 3D hand path length, revealing significant differences between expertsand novice surgeons, emphasizing the potential of depth cameras in capturingsurgical skills. We also investigated the influence of camera angles onmeasurement accuracy, highlighting the advantages of 3D cameras in providing amore accurate representation of hand movements.  Conclusion: Our research contributes to advancing the field of surgical skillassessment by leveraging depth cameras for more reliable and privacyevaluations. The findings suggest that depth cameras can be valuable inassessing surgical skills and provide a foundation for future research in thisarea.

Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera