Abstract
Background:In microsurgery, integrating motor and visual information is crucial for achieving precise bimanual maneuvers. Computer vision serves as a useful tool for analyzing surgical maneuvers and enhancing situational awareness in medical assistive systems. Furthermore, the combination of deep learning with computer vision shows promise for objectively assessing surgical skills. Methods:This study compared two methods for tracking 3D movement during microsurgery: Mitracks3D, a stereoscopic tracker using color-marked instruments, and a deep learning method for instruments identification based on YOLOv8. Additionally, drawing an analogy between single-camera and new stereoscopic recording systems, 2D and 3D motion signals from microsurgery training videos on the peg transfer task were obtained to analyze surgeons’ psychomotor skills using six motion analysis parameters (MAPs). Results:When comparing the two 3D tracking methods, the motion signals revealed a relative error (ϵ) of less than 10%, with a correlation coefficient (ρ) exceeding 0.9892 ±0.0020, tracking loss below 2.0398%, and instruments detection time under 1 ms for Mitracks3D and 7.09 ±0.62 ms for YOLOv8. When comparing the performance between participants using 2D and 3D signals, statistically significant differences were found in 4 of the 6 explored MAPs. Additionally, a high cosine similarity (cs>0.99) was found between the performance scores of the 6 MAPs, indicating compatibility between the 2D and 3D recording modalities. Conclusions:The deep learning-based approach is as effective as stereo-tracking with markers; both methods produced remarkably similar 3D motion signals. The compatibility in evaluation between 2D and 3D was confirmed. In essence, adopting computer vision methods such as Mitracks3D and YOLOv8 provides a compelling alternative for constructing objective assessment tools for surgical skills.
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