Human-AI Collaboration and Explainability for 2D/3D Registration Quality Assurance

TL;DR

This paper introduces an AI model with explainability for assessing 2D/3D registration quality, demonstrating that human-AI collaboration improves detection sensitivity and user understanding, enhancing surgical safety.

Contribution

It presents the first AI model with explainability tailored for 2D/3D registration quality assessment and systematically evaluates collaborative decision-making effects.

Findings

AI-only achieves highest accuracy

Collaborative approaches improve sensitivity, precision, and specificity

Workload decreases in collaborative conditions

Abstract

Purpose: As surgery increasingly integrates advanced imaging, algorithms, and robotics to automate complex tasks, human judgment of system correctness remains a vital safeguard for patient safety. A critical example is 2D/3D registration, where small registration misalignments can lead to surgical errors. Current visualization strategies alone are insufficient to reliably enable humans to detect these misalignments, highlighting the need for enhanced decision-support tools. Methods: We propose the first artificial intelligence (AI) model tailored to 2D/3D registration quality assessment, augmented with explainable AI (XAI) mechanisms to clarify the model's predictions. Using both objective measures (e.g., accuracy, sensitivity, precision, specificity) and subjective evaluations (e.g., workload, trust, and understanding), we systematically compare decision-making across four conditions: AI-only, Human-only, Human+AI, and Human+XAI. Results: The AI-only condition achieved the highest accuracy, whereas collaborative paradigms (Human+AI and Human+XAI) improved sensitivity, precision, and specificity compared to standalone approaches. Participants experienced significantly lower workload in collaborative conditions relative to the Human-only condition. Moreover, participants reported a greater understanding of AI predictions in the Human+XAI condition than in Human+AI, although no significant differences were observed between the two collaborative paradigms in perceived trust or workload. Conclusion: Human-AI collaboration can enhance 2D/3D registration quality assurance, with explainability mechanisms improving user understanding. Future work should refine XAI designs to optimize decision-making performance and efficiency. Extending both the algorithmic design and human-XAI collaboration elements holds promise for more robust quality assurance of 2D/3D registration.