  Accurate surgery duration estimation is necessary for optimal OR planning,
which plays an important role in patient comfort and safety as well as resource
optimization. It is, however, challenging to preoperatively predict surgery
duration since it varies significantly depending on the patient condition,
surgeon skills, and intraoperative situation. In this paper, we propose a deep
learning pipeline, referred to as RSDNet, which automatically estimates the
remaining surgery duration (RSD) intraoperatively by using only visual
information from laparoscopic videos. Previous state-of-the-art approaches for
RSD prediction are dependent on manual annotation, whose generation requires
expensive expert knowledge and is time-consuming, especially considering the
numerous types of surgeries performed in a hospital and the large number of
laparoscopic videos available. A crucial feature of RSDNet is that it does not
depend on any manual annotation during training, making it easily scalable to
many kinds of surgeries. The generalizability of our approach is demonstrated
by testing the pipeline on two large datasets containing different types of
surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental
results also show that the proposed network significantly outperforms a
traditional method of estimating RSD without utilizing manual annotation.
Further, this work provides a deeper insight into the deep learning network
through visualization and interpretation of the features that are automatically
learned.
