Robotic-assisted surgeries benefit both surgeons and patients, however, surgeons frequently need to adjust the endoscopic camera to achieve good viewpoints. Simultaneously controlling the camera and the surgical instruments is impossible, and consequentially, these camera adjustments repeatedly interrupt the surgery. Autonomous camera control could help overcome this challenge, but most existing systems are reactive, e.g., by having the camera follow the surgical instruments. We propose a predictive approach for anticipating when camera movements will occur using artificial neural networks. We used the kinematic data of the surgical instruments, which were recorded during robotic-assisted surgical training on porcine models. We split the data into segments, and labeled each either as a segment that immediately precedes a camera movement, or one that does not. Due to the large class imbalance, we trained an ensemble of networks, each on a balanced sub-set of the training data. We found that the instruments' kinematic data can be used to predict when camera movements will occur, and evaluated the performance on different segment durations and ensemble sizes. We also studied how much in advance an upcoming camera movement can be predicted, and found that predicting a camera movement 0.25, 0.5, and 1 second before they occurred achieved 98%, 94%, and 84% accuracy relative to the prediction of an imminent camera movement. This indicates that camera movement events can be predicted early enough to leave time for computing and executing an autonomous camera movement and suggests that an autonomous camera controller for RAMIS may one day be feasible.

翻译：然而,外科医生经常需要调整内窥镜摄像机的运动数据,以取得良好的视角。同时控制相机和外科仪器是不可能的,因此,这些相机的调整会一再打断手术。自动相机控制可以帮助克服这一挑战,但大多数现有系统都是反应性的,例如,让相机跟随外科仪器。我们建议了一种预测方法,以预测相机运动何时会使用人工神经网络进行。我们使用了外科手术仪器的动能数据,这些数据是在机器人协助的外科手术训练期间记录到的。我们把数据分成部分,将每个部分贴上标签,或者贴上紧接照相机运动之前的部分,或者不贴上。由于大规模阶级不平衡,我们训练了网络的组合,每个部分都使用平衡的外科器。我们发现,这些仪器的动能数据可以用来预测相机运动何时会发生,并且评估不同片段和孔径型摄影机模型模型的性能。我们还研究了如何提前将数据分解成一段段,同时将每个部分标定出每个部分的部位标为一个部位的部位段运动,一个预估到第98号的直径直径直径直至第一位的直径直径。