# Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation

arXiv: https://arxiv.org/abs/2304.09285 · HTML: https://mlsanity.com/paper/2304.09285/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [OperA: Attention-Regularized Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2103.03873/) (2021)
- [DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures](https://mlsanity.com/paper/1803.08606/) (2018)
- [DeepPhase: Surgical Phase Recognition in CATARACTS Videos](https://mlsanity.com/paper/1807.10565/) (2018)
- [Towards Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2212.04582/) (2022)
- [X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery](https://mlsanity.com/paper/1803.08608/) (2018)
- [Virtual Reality for Synergistic Surgical Training and Data Generation](https://mlsanity.com/paper/2111.08097/) (2021)
- [SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments](https://mlsanity.com/paper/2206.06127/) (2022)
- [Real-time Surgical Tools Recognition in Total Knee Arthroplasty Using Deep Neural Networks](https://mlsanity.com/paper/1806.02031/) (2018)

## Built on by (most cited first, 8 of 8)

- [FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation](https://mlsanity.com/paper/2403.08059/) (2024)
- [Neural Finite-State Machines for Surgical Phase Recognition](https://mlsanity.com/paper/2411.18018/) (2024)
- [MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognition](https://mlsanity.com/paper/2503.00695/) (2025)
- [Egosurg: Arbitrary view synthesis for egocentric replay of operating room workflows from ambient cameras](https://mlsanity.com/paper/2510.04802/) (2025)
- [Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures](https://mlsanity.com/paper/2511.03882/) (2025)
- [3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering](https://mlsanity.com/paper/2512.05803/) (2025)
- [2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models](https://mlsanity.com/paper/2606.21414/) (2026)
- [Patient-Specific Articulated Digital Twins from a Single Full-Body CT Scan](https://mlsanity.com/paper/2607.02156/) (2026)

## Abstract (arXiv)

Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity -- corridor, activity, view, and frame value -- simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases at the four granularity levels. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 93.8% on simulated sequences and 67.57% in cadaver across all granularity levels, with up to 88% accuracy for the target corridor in real data. This work constitutes the first step toward SPR for the X-ray domain, establishing an approach to categorizing phases in X-ray-guided surgery, simulating realistic image sequences to enable machine learning model development, and demonstrating that this approach is feasible for the analysis of real procedures. As X-ray-based SPR continues to mature, it will benefit procedures in orthopedic surgery, angiography, and interventional radiology by equipping intelligent surgical systems with situational awareness in the operating room.