  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.
