用历史手术流程预测鼻内垂体手术剩余时间，提升麻醉安排与手术排程效率。
PitRSDNet: Predicting Intra-operative Remaining Surgery Duration in Endoscopic Pituitary Surgery
- 基于手术流程序列的时空神经网络，联合预测步骤与剩余时长。
- 在88例视频数据上表现优于传统方法，尤其对异常病例精度提升显著。
- 适合手术调度优化、智能麻醉系统研发人员参考。
准确的术中剩余手术时长（RSD）预测有助于麻醉师更精准地决定麻醉药物给药时机，并通知医院人员准备下一例手术，从而提升患者照护质量并降低手术室运营成本。鼻内垂体手术因流程序列可变、存在可选步骤，导致手术时长差异大，预测难度高。本文提出PitRSDNet，一种基于历史数据学习手术流程序列的时空神经网络模型。该模型通过两种方式融合流程知识：1）多任务学习，同时预测当前步骤与剩余时长；2）在时序建模中引入前序步骤作为上下文信息。模型在包含88个手术视频的新数据集上训练与评估，性能超越以往统计与机器学习方法。结果表明，利用前序步骤知识能有效提升异常病例的预测精度。
原文摘要 · Abstract (English)
Accurate intra-operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore RSD plays an important role in improving patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration. This paper presents PitRSDNet for predicting RSD during pituitary surgery, a spatio-temporal neural network model that learns from historical data focusing on workflow sequences. PitRSDNet integrates workflow knowledge into RSD prediction in two forms: 1) multi-task learning for concurrently predicting step and RSD; and 2) incorporating prior steps as context in temporal learning and inference. PitRSDNet is trained and evaluated on a new endoscopic pituitary surgery dataset with 88 videos to show competitive performance improvements over previous statistical and machine learning methods. The findings also highlight how PitRSDNet improve RSD precision on outlier cases utilising the knowledge of prior steps.
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