用于白内障手术基于视频的技能评估的可解释人工智能驱动框架

中文摘要 AI 辅助

外科劳动力的持续短缺以及传统培训方法的固有局限性，凸显了外科教育中自动化、数据驱动方法的必要性。本研究通过引入一种新颖的、可解释人工智能驱动的自动化技能评估框架来应对这些挑战，特别聚焦于白内障手术。我们提供了全球最大的白内障手术视频数据集，包含2000条录像。此外，我们提出了一种人工智能驱动的分析框架，该框架采用先进的计算机视觉和信号处理技术自动评估手术视频，以得出客观、定量的性能指标，可补充或潜在替代主观评分方法。与以往方法相比，我们框架的显著优势在于其输出的可解释性，使其超越了单纯不透明的技能分类工具。通过对83段白内障手术视频的实验分析，我们证明自动计算的指标与专家主观评估具有强相关性，在手术技能评估中达到了高达87%的准确率。我们对每个指标进行了单独检验，外科专家使用新引入的囊膜切开术技能评估系统（Capsulorhexis Skill Assessment System, CSAS）提供了主观评分，将这些主观评估与通过我们框架提取的10个基于运动的客观指标进行比较，结果表明主观评分与自动化指标之间存在稳健的相关性，凸显了该框架准确模拟外科专业能力的能力。

英文摘要

Persistent shortages in the surgical workforce and inherent limitations of traditional training methods highlight the necessity of automated, data-driven approaches in surgical education. This study addresses these challenges by introducing a novel, explainable AI-powered framework for automated skill assessment, specifically focusing on cataract surgery. We present the world's largest dataset of cataract surgery videos, comprising 2,000 recordings. Additionally, we propose an AI-powered analytical framework that employs advanced computer vision and signal-processing techniques to automatically evaluate surgical videos to derive objective, quantitative performance indicators that complement or potentially replace subjective scoring methods. A significant advantage of our framework over previous methods lies precisely in its explainability of outputs, elevating it beyond merely an opaque skill classification tool. Through experimental analysis of 83 cataract surgery videos, we demonstrate that the automatically computed metrics exhibit strong correlations with expert-based subjective evaluations, achieving up to 87% accuracy in surgical skill assessment. Each metric was individually examined, and expert surgeons provided subjective ratings using the newly introduced Capsulorhexis Skill Assessment System (CSAS). These subjective assessments were compared with ten objective motion-based metrics extracted through our framework. The results indicated a robust correlation between subjective ratings and automated indicators, underscoring the framework's capacity to accurately model surgical expertise.

发表机构

• K.N. Toosi University of Technology（霍加托西大学）
• Faculties of Electrical and Computer Engineering（电气与计算机工程学院）
• Applied Robotics and AI Solutions (ARAS)（应用机器人与人工智能解决方案（ARAS））

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