Guo Rui, Perreault Conor, Mueller Benjamin, Liu Xi, Jarc Anthony
Digital Solutions, Intuitive Surgical, Peachtree Corners, GA, 30092, USA.
Int J Comput Assist Radiol Surg. 2025 Jun 26. doi: 10.1007/s11548-025-03442-w.
Artificial intelligence is transforming surgical practices by improving procedural quality and decision-making. Machine learning-based video analysis can reliably identify surgical milestones, enhancing contextual understanding for surgeons. This study proposes a novel framework for detecting critical view of safety (CVS) in robot-assisted laparoscopic cholecystectomy (RLC) to improve procedural safety.
We present a meta-auxiliary learning framework that delicately combines milestone recognition and anatomical segmentation to enhance contextual awareness. The framework addresses label imbalance by facilitating knowledge sharing across tasks, ensuring balanced optimization. A curated RLC dataset was utilized to evaluate CVS identification and multi-instance segmentation performance.
The proposed method achieved an F1 score of 78% for CVS detection and a mean IOU of 83.9% for anatomical segmentation, demonstrating its efficacy in complex surgical environments.
This framework establishes a new paradigm for surgical video analysis by integrating milestone detection and segmentation. Its ability to enhance decision support and procedural review in RLC highlights its potential for broader adoption in clinical practice.
人工智能正在通过提高手术质量和决策能力来改变外科手术实践。基于机器学习的视频分析能够可靠地识别手术关键节点，增强外科医生的情境理解。本研究提出了一种用于在机器人辅助腹腔镜胆囊切除术（RLC）中检测安全关键视野（CVS）的新框架，以提高手术安全性。
我们提出了一种元辅助学习框架，该框架巧妙地结合了关键节点识别和解剖分割，以增强情境感知。该框架通过促进跨任务的知识共享来解决标签不平衡问题，确保平衡优化。使用一个经过整理的RLC数据集来评估CVS识别和多实例分割性能。
所提出的方法在CVS检测方面达到了78%的F1分数，在解剖分割方面的平均交并比为83.9%，证明了其在复杂手术环境中的有效性。
该框架通过整合关键节点检测和分割，为手术视频分析建立了一种新范式。其在RLC中增强决策支持和手术回顾的能力突出了其在临床实践中更广泛应用的潜力。
