PitRSDNet: Predicting Intra-operative Remaining Surgery Duration in Endoscopic Pituitary Surgery
This work addresses a domain-specific problem for anaesthetists and hospital staff in pituitary surgery, with incremental improvements in RSD prediction.
The paper tackles the problem of predicting intra-operative remaining surgery duration (RSD) in endoscopic pituitary surgery to improve patient care and scheduling efficiency, and presents PitRSDNet, a spatio-temporal neural network that achieves competitive performance improvements over previous methods on a dataset of 88 videos.
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.