Pringle maneuver (PM) in laparoscopic liver resection aims to reduce blood loss and provide a clear surgical view by intermittently blocking blood inflow of the liver, whereas prolonged PM may cause ischemic injury. To comprehensively monitor this surgical procedure and provide timely warnings of ineffective and prolonged blocking, we suggest two complementary AI-assisted surgical monitoring tasks: workflow recognition and blocking effectiveness detection in liver resections. The former presents challenges in real-time capturing of short-term PM, while the latter involves the intraoperative discrimination of long-term liver ischemia states. To address these challenges, we meticulously collect a novel dataset, called PmLR50, consisting of 25,037 video frames covering various surgical phases from 50 laparoscopic liver resection procedures. Additionally, we develop an online baseline for PmLR50, termed PmNet. This model embraces Masked Temporal Encoding (MTE) and Compressed Sequence Modeling (CSM) for efficient short-term and long-term temporal information modeling, and embeds Contrastive Prototype Separation (CPS) to enhance action discrimination between similar intraoperative operations. Experimental results demonstrate that PmNet outperforms existing state-of-the-art surgical workflow recognition methods on the PmLR50 benchmark. Our research offers potential clinical applications for the laparoscopic liver surgery community. Source code and data will be publicly available.

翻译：腹腔镜肝切除术中的Pringle手法（PM）通过间歇性阻断肝脏血流以减少失血并提供清晰手术视野，但长时间PM可能导致缺血性损伤。为全面监控该手术过程并及时预警无效及过长的血流阻断，我们提出两项互补的AI辅助手术监测任务：肝切除术中的手术流程识别与阻断效果检测。前者在实时捕捉短期PM方面存在挑战，后者则涉及术中长期肝脏缺血状态的判别。为解决这些难题，我们精心收集了名为PmLR50的新型数据集，包含来自50例腹腔镜肝切除术的25,037帧视频图像，涵盖不同手术阶段。此外，我们为PmLR50开发了在线基线模型PmNet。该模型采用掩码时序编码（MTE）与压缩序列建模（CSM）以实现高效的短期与长期时序信息建模，并嵌入对比原型分离（CPS）机制以增强相似术中操作间的动作区分能力。实验结果表明，PmNet在PmLR50基准测试中优于现有最先进的手术流程识别方法。本研究为腹腔镜肝脏外科领域提供了潜在的临床应用价值。源代码与数据将公开提供。