# Conformal forecasting for surgical instrument trajectory

arXiv: https://arxiv.org/abs/2503.04191 · HTML: https://mlsanity.com/paper/2503.04191/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction](https://mlsanity.com/paper/2403.07263/) (2024)
- [PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery](https://mlsanity.com/paper/2409.01184/) (2024)
- [Max-Rank: Efficient Multiple Testing for Conformal Prediction](https://mlsanity.com/paper/2311.10900/) (2023)
- [Robust Conformal Volume Estimation in 3D Medical Images](https://mlsanity.com/paper/2407.19938/) (2024)
- [Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography](https://mlsanity.com/paper/2409.09680/) (2024)
- [Vision-Based Neurosurgical Guidance: Unsupervised Localization and Camera-Pose Prediction](https://mlsanity.com/paper/2405.09355/) (2024)
- [Anatomy Might Be All You Need: Forecasting What to Do During Surgery](https://mlsanity.com/paper/2501.18011/) (2025)

## Built on by (most cited first, 3 of 3)

- [Trustworthy Endoscopic Super-Resolution](https://mlsanity.com/paper/2604.18001/) (2026)
- [Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation](https://mlsanity.com/paper/2606.23131/) (2026)
- [Scaling Full Conformal Image Classifiers](https://mlsanity.com/paper/2609.37298/) (2026)

## Abstract (arXiv)

Forecasting surgical instrument trajectories and predicting the next surgical action recently started to attract attention from the research community. Both these tasks are crucial for automation and assistance in endoscopy surgery. Given the safety-critical nature of these tasks, reliable uncertainty quantification is essential. Conformal prediction is a fast-growing and widely recognized framework for uncertainty estimation in machine learning and computer vision, offering distribution-free, theoretically valid prediction intervals. In this work, we explore the application of standard conformal prediction and conformalized quantile regression to estimate uncertainty in forecasting surgical instrument motion, i.e., predicting direction and magnitude of surgical instruments' future motion. We analyze and compare their coverage and interval sizes, assessing the impact of multiple hypothesis testing and correction methods. Additionally, we show how these techniques can be employed to produce useful uncertainty heatmaps. To the best of our knowledge, this is the first study applying conformal prediction to surgical guidance, marking an initial step toward constructing principled prediction intervals with formal coverage guarantees in this domain.