Conformal forecasting for surgical instrument trajectory

2025-03-06

AI Summary

This work addresses the challenge of quantifying uncertainty in instrument trajectory and next-action prediction for autonomous endoscopic surgery. We propose the first conformalized quantile regression method tailored for surgical navigation, introducing conformal prediction—previously unexplored in this domain—to motion forecasting. Our approach incorporates a task-specific calibration strategy to construct prediction intervals and uncertainty heatmaps with rigorous statistical guarantees (e.g., guaranteed 1−α coverage). To enhance reliability in multi-step forecasting, we integrate multiple hypothesis testing correction. Experiments demonstrate that our method achieves theoretically valid coverage on trajectory prediction tasks, significantly improving the reliability, interpretability, and clinical safety of uncertainty estimation. By providing verifiable, statistically grounded uncertainty modeling, this work establishes a foundational framework for trustworthy intelligent surgical assistance systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Forecasting surgical instrument trajectories for automation.
• Predicting next surgical actions with uncertainty quantification.
• Applying conformal prediction to surgical guidance for reliable intervals.

Innovation

• Conformal prediction for surgical trajectory forecasting
• Uncertainty quantification via conformalized quantile regression
• Generating uncertainty heatmaps for surgical guidance