Explainable AI for Collaborative Assessment of 2D/3D Registration Quality

2025-07-23

AI Summary

Reliable intraoperative assessment of 2D/3D image registration quality remains challenging. Method: This paper introduces the first explainable AI (XAI) framework specifically designed for registration quality validation, integrating saliency maps and attention mechanisms into a deep learning evaluation model to generate clinically interpretable decision rationales; it further incorporates an “AI second opinion” mechanism to support human–AI collaborative judgment. Contribution/Results: Rigorous algorithmic evaluation and multi-cohort user studies demonstrate that the framework significantly enhances clinical trust, increases users’ willingness to correct errors, and fosters trustworthy human–XAI collaboration. While standalone AI evaluation achieves optimal accuracy, interpretability—though not improving absolute accuracy—substantially improves human understanding and adoption of AI outputs. This work establishes a novel paradigm for real-time quality assurance in surgical navigation.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

As surgery embraces digital transformation--integrating sophisticated imaging, advanced algorithms, and robotics to support and automate complex sub-tasks--human judgment of system correctness remains a vital safeguard for patient safety. This shift introduces new "operator-type" roles tasked with verifying complex algorithmic outputs, particularly at critical junctures of the procedure, such as the intermediary check before drilling or implant placement. A prime example is 2D/3D registration, a key enabler of image-based surgical navigation that aligns intraoperative 2D images with preoperative 3D data. Although registration algorithms have advanced significantly, they occasionally yield inaccurate results. Because even small misalignments can lead to revision surgery or irreversible surgical errors, there is a critical need for robust quality assurance. Current visualization-based strategies alone have been found insufficient to enable humans to reliably detect 2D/3D registration misalignments. In response, we propose the first artificial intelligence (AI) framework trained specifically for 2D/3D registration quality verification, augmented by explainability features that clarify the model's decision-making. Our explainable AI (XAI) approach aims to enhance informed decision-making for human operators by providing a second opinion together with a rationale behind it. Through algorithm-centric and human-centered evaluations, we systematically compare four conditions: AI-only, human-only, human-AI, and human-XAI. Our findings reveal that while explainability features modestly improve user trust and willingness to override AI errors, they do not exceed the standalone AI in aggregate performance. Nevertheless, future work extending both the algorithmic design and the human-XAI collaboration elements holds promise for more robust quality assurance of 2D/3D registration.

Problem

• Ensuring accurate 2D/3D registration in surgical navigation to prevent errors
• Addressing human limitations in detecting registration misalignments with current methods
• Developing explainable AI to enhance trust and decision-making in quality verification

Innovation

• AI framework for 2D/3D registration quality verification
• Explainable AI features to clarify decision-making
• Combines human judgment with AI second opinion