# CataNet: Predicting remaining cataract surgery duration

arXiv: https://arxiv.org/abs/2106.11048 · HTML: https://mlsanity.com/paper/2106.11048/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [DeepPhase: Surgical Phase Recognition in CATARACTS Videos](https://mlsanity.com/paper/1807.10565/) (2018)
- [RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations](https://mlsanity.com/paper/1802.03243/) (2018)
- [Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration](https://mlsanity.com/paper/2002.11367/) (2020)
- [Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data](https://mlsanity.com/paper/1811.03384/) (2018)

## Built on by (most cited first, 15 of 15)

- [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)
- [ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.12448/) (2021)
- [Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection](https://mlsanity.com/paper/2312.06295/) (2023)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [Towards Graph Representation Learning Based Surgical Workflow Anticipation](https://mlsanity.com/paper/2208.03824/) (2022)
- [SWAG: Long-term Surgical Workflow Prediction with Generative-based Anticipation](https://mlsanity.com/paper/2412.18849/) (2024)
- [PitRSDNet: Predicting Intra-operative Remaining Surgery Duration in Endoscopic Pituitary Surgery](https://mlsanity.com/paper/2409.16998/) (2024)
- [Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation](https://mlsanity.com/paper/2412.06454/) (2024)
- [Is there progress in activity progress prediction?](https://mlsanity.com/paper/2308.05533/) (2023)
- [Multiview Progress Prediction of Robot Activities](https://mlsanity.com/paper/2603.00151/) (2026)
- [A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images](https://mlsanity.com/paper/2107.12093/) (2021)
- [DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.05352/) (2021)
- [CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition](https://mlsanity.com/paper/2503.10216/) (2025)
- [WetCat: Enabling Automated Skill Assessment in Wet-Lab Cataract Surgery Videos](https://mlsanity.com/paper/2506.08896/) (2025)
- [State-Change Learning for Prediction of Future Events in Endoscopic Videos](https://mlsanity.com/paper/2510.12904/) (2025)

## Abstract (arXiv)

Cataract surgery is a sight saving surgery that is performed over 10 million times each year around the world. With such a large demand, the ability to organize surgical wards and operating rooms efficiently is critical to delivery this therapy in routine clinical care. In this context, estimating the remaining surgical duration (RSD) during procedures is one way to help streamline patient throughput and workflows. To this end, we propose CataNet, a method for cataract surgeries that predicts in real time the RSD jointly with two influential elements: the surgeon's experience, and the current phase of the surgery. We compare CataNet to state-of-the-art RSD estimation methods, showing that it outperforms them even when phase and experience are not considered. We investigate this improvement and show that a significant contributor is the way we integrate the elapsed time into CataNet's feature extractor.