Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation
2026-06-22 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how to automatically evaluate how well surgeons control laparoscopic cameras during surgery, which is usually done manually and takes a lot of time. They created a list of 14 important camera skills grouped into categories like framing, clarity, stability, motion, and safety. They surveyed surgeons to find out which skills matter most and compared this with how well computer vision techniques can currently measure those skills. Their work highlights which skills are both important and ready for automatic assessment, helping guide future development of AI tools to support surgical training and improve safety.
laparoscopic camera navigationcomputer visionsurgical skill assessmentFraming & CompositionVisibility & ClarityOrientation & StabilityMotion & DynamicsSafety & AwarenessLikert scaletechnological readiness
Authors
Amir Ebrahimzadeh, Nazila Esmaeili, Michael Ghadimi, Jannis Hagenah
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.