Advances in the medical field have increased the need to incorporate modern techniques into surgical resident training and surgical skills learning. To facilitate this integration, one approach that has gained credibility is the incorporation of simulator based training to supplement traditional training programs. However, existing implementations of these training methods still require the constant presence of a competent surgeon to assess the surgical dexterity of the trainee, which limits the evaluation methods and relies on subjective evaluation. This research proposes an efficient, effective, and economic video-based skill assessment technique for minimally invasive surgery (MIS). It analyzes a surgeon's hand and surgical tool movements and detects features like smoothness, efficiency, and preciseness. The system is capable of providing both real time on-screen feedback and a performance score at the end of the surgery. Finally, we present a web-based tool where surgeons can securely upload MIS training videos and receive evaluation scores and an analysis of trainees' performance trends over time.

No abstract

No abstract

No abstract

This research proposes a novel computer-vision-based approach for skill assessment by observing a surgeon's hand and surgical tool movements in minimally invasive surgical training, which can be extended to the evaluation in real surgeries. Videos capturing the surgical field are analyzed using a system composed of a series of computer vision algorithms. The system automatically detects major skill measuring features from surgical task videos and provides real-time performance feedback on objective and quantitative measurement of surgical skills.