Computer Vision for Assessing Surgical Movements in Neurosurgery.

Objective evaluation of microsurgical technique quality is vital for successful training in neurosurgery. This study aimed to assess the accuracy of automatically detecting a neurosurgeon's proper posture and hand positioning using computer vision. We employed the RTMPose neural network model to identify key anatomical points in the neurosurgeon's projection and calculated various angles formed by connecting these points. By utilizing machine learning on these angles, we were able to classify images of the surgeon's posture and hands into correct positions and various types of errors with an accuracy of at least 0.9. Computer vision enables successful detection and objective assessment of the neurosurgeon's posture and hand positions. The high accuracy of this detection can pave the way for a new training approach in neurosurgery.