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.

翻译：术中剩余手术时长（RSD）的准确预测，有助于麻醉师更精确地决定何时施用麻醉剂和药物，并通知医院工作人员安排下一位患者。因此，RSD在通过高效调度改善患者护理和最小化手术室成本方面发挥着重要作用。在内窥镜垂体手术中，由于可变的工作流程序列以及可选步骤的选择导致手术时长具有高度可变性，这使得RSD预测面临独特的挑战。本文提出了用于垂体手术期间RSD预测的PitRSDNet，这是一种从专注于工作流程序列的历史数据中学习的时空神经网络模型。PitRSDNet以两种形式将工作流程知识整合到RSD预测中：1）用于同时预测手术步骤和RSD的多任务学习；2）在时序学习和推理中纳入先前步骤作为上下文信息。PitRSDNet在一个包含88个视频的新内窥镜垂体手术数据集上进行了训练和评估，结果表明其性能相较于以往的统计和机器学习方法有显著提升。研究结果还强调了PitRSDNet如何利用先前步骤的知识来提高异常病例的RSD预测精度。