# PitRSDNet: Predicting Intra-operative Remaining Surgery Duration in Endoscopic Pituitary Surgery

arXiv: https://arxiv.org/abs/2409.16998 · HTML: https://mlsanity.com/paper/2409.16998/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations](https://mlsanity.com/paper/1802.03243/) (2018)
- [On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis](https://mlsanity.com/paper/2203.07976/) (2022)
- [CataNet: Predicting remaining cataract surgery duration](https://mlsanity.com/paper/2106.11048/) (2021)
- [Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows](https://mlsanity.com/paper/2009.00681/) (2020)
- [PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery](https://mlsanity.com/paper/2409.01184/) (2024)

## Built on by (most cited first, 2 of 2)

- [SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation](https://mlsanity.com/paper/2412.18849/) (2024)
- [Scaling Video Pretraining for Surgical Foundation Models](https://mlsanity.com/paper/2603.29966/) (2026)

## Abstract (arXiv)

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.