🤖 AI Summary
This study addresses the longstanding reliance on subjective and inefficient manual scoring in assessing laparoscopic camera navigation skills, which lacks standardized and scalable objective metrics. The authors propose a novel evaluation taxonomy comprising 14 key elements, aligning clinical importance—established through expert consensus—with technical readiness of computer vision methods via a “clinical importance–technical readiness” matrix to prioritize automation targets. Through Likert-scale surveys, expert-based skill rankings, and computer vision–derived automated measurements, validated across 23 surgeons, the study identifies high-priority metrics such as field-of-view coverage, focus quality, and instrument centering. These metrics jointly satisfy clinical relevance and technical feasibility, establishing a practical framework for AI-driven surgical training assessment.
📝 Abstract
Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are time-consuming and difficult to scale. Automated feedback could significantly enhance surgical training by providing immediate, standardized metrics. This study aims to define, clinically evaluate the relevance, and establish the technical readiness of a set of approaches for LCN assessment.
Methods: We developed a detailed taxonomy of 14 key aspects of camera navigation, categorized into Framing & Composition, Visibility & Clarity, Orientation & Stability, Motion & Dynamics, and Safety & Awareness. For each aspect, we assessed the technological readiness of automated measurement based on the current state of the art (SoTA) in computer vision (CV). To establish clinical relevance, we designed a survey for practicing laparoscopic surgeons to rate the importance of each aspect on a 5-point Likert scale and to select the five most critical skills.
Results: 23 surgeons participated in the survey. Foundational aspects like Field of View, Focus and Centering were rated as most important by surgeons. We present a "Clinical Importance vs. CV Technological Readiness" matrix, identifying high-priority targets for development--aspects that are both clinically crucial and technologically ready to measure.
Conclusion: This work establishes a foundational framework for quantifying LCN skills. By aligning surgeon priorities with CV capabilities, we provide a clear roadmap for automatic skill assessment. This foundation enables the development of AI-driven assistance tools that can accelerate the learning curve for surgical assistants and potentially improve surgical safety and efficiency.